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Posted to general@incubator.apache.org by Andrew Purtell <ap...@apache.org> on 2016/05/23 22:22:20 UTC
[VOTE] Accept PredictionIO into the Apache Incubator
Since discussion on the matter of PredictionIO has died down, I would like
to call a VOTE
on accepting PredictionIO into the Apache Incubator.
Proposal: https://wiki.apache.org/incubator/PredictionIO
[ ] +1 Accept PredictionIO into the Apache Incubator
[ ] +0 Abstain
[ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
This vote will be open for at least 72 hours.
My vote is +1 (binding)
--
PredictionIO Proposal
Abstract
PredictionIO is an open source Machine Learning Server built on top of
state-of-the-art open source stack, that enables developers to manage and
deploy production-ready predictive services for various kinds of machine
learning tasks.
Proposal
The PredictionIO platform consists of the following components:
* PredictionIO framework - provides the machine learning stack for
building, evaluating and deploying engines with machine learning
algorithms. It uses Apache Spark for processing.
* Event Server - the machine learning analytics layer for unifying events
from multiple platforms. It can use Apache HBase or any JDBC backends
as its data store.
The PredictionIO community also maintains a Template Gallery, a place to
publish and download (free or proprietary) engine templates for different
types of machine learning applications, and is a complemental part of the
project. At this point we exclude the Template Gallery from the proposal,
as it has a separate set of contributors and we’re not familiar with an
Apache approved mechanism to maintain such a gallery.
Background
PredictionIO was started with a mission to democratize and bring machine
learning to the masses.
Machine learning has traditionally been a luxury for big companies like
Google, Facebook, and Netflix. There are ML libraries and tools lying
around the internet but the effort of putting them all together as a
production-ready infrastructure is a very resource-intensive task that is
remotely reachable by individuals or small businesses.
PredictionIO is a production-ready, full stack machine learning system that
allows organizations of any scale to quickly deploy machine learning
capabilities. It comes with official and community-contributed machine
learning engine templates that are easy to customize.
Rationale
As usage and number of contributors to PredictionIO has grown bigger and
more diverse, we have sought for an independent framework for the project
to keep thriving. We believe the Apache foundation is a great fit. Joining
Apache would ensure that tried and true processes and procedures are in
place for the growing number of organizations interested in contributing
to PredictionIO. PredictionIO is also a good fit for the Apache foundation.
PredictionIO was built on top of several Apache projects (HBase, Spark,
Hadoop). We are familiar with the Apache process and believe that the
democratic and meritocratic nature of the foundation aligns with the
project goals.
Initial Goals
The initial milestones will be to move the existing codebase to Apache and
integrate with the Apache development process. Once this is accomplished,
we plan for incremental development and releases that follow the Apache
guidelines, as well as growing our developer and user communities.
Current Status
PredictionIO has undergone nine minor releases and many patches.
PredictionIO is being used in production by Salesforce.com as well as many
other organizations and apps. The PredictionIO codebase is currently
hosted at GitHub, which will form the basis of the Apache git repository.
Meritocracy
We plan to invest in supporting a meritocracy. We will discuss the
requirements in an open forum. We intend to invite additional developers
to participate. We will encourage and monitor community participation so
that privileges can be extended to those that contribute.
Community
Acceptance into the Apache foundation would bolster the already strong
user and developer community around PredictionIO. That community includes
many contributors from various other companies, and an active mailing list
composed of hundreds of users.
Core Developers
The core developers of our project are listed in our contributors and
initial PPMC below. Though many are employed at Salesforce.com, there are
also engineers from ActionML, and independent developers.
Alignment
The ASF is the natural choice to host the PredictionIO project as its goal
is democratizing Machine Learning by making it more easily accessible to
every user/developer. PredictionIO is built on top of several top level
Apache projects as outlined above.
Known Risks
Orphaned Products
PredictionIO has a solid and growing community. It is deployed on
production environments by companies of all sizes to run various kinds of
predictive engines.
In addition to the community contribution to PredictionIO framework, the
community is also actively contributing new engines to the Template
Gallery as well as SDKs and documentation for the project. Salesforce is
committed to utilize and advance the PredictionIO code base and support
its user community.
Inexperience with Open Source
PredictionIO has existed as a healthy open source project for almost two
years and is the most starred Scala project on GitHub. All of the proposed
committers have contributed to ASF and Linux Foundation open source
projects. Several current committers on Apache projects and Apache Members
are involved in this proposal and intend to provide mentorship.
Homogeneous Developers
The initial list of committers includes developers from several
institutions, including Salesforce, ActionML, Channel4, USC as well as
unaffiliated developers.
Reliance on Salaried Developers
Like most open source projects, PredictionIO receives substantial support
from salaried developers. PredictionIO development is partially supported
by Salesforce.com, but there are many contributors from various other
companies, and an active mailing list composed of hundreds of users. We
will continue our efforts to ensure stewardship of the project to be
independent of salaried developers by meritocratically promoting those
contributors to committers.
Relationships with Other Apache Product
PredictionIO relies heavily on top level Apache projects such as Apache
Spark, HBase and Hadoop. However it brings a distinguished functionality,
rather than just an abstraction - Machine Learning in a plug-and-play
fashion.
Compared to Apache Mahout, which focuses on the development of a wide
variety of algorithms, PredictionIO offers a platform to manage the whole
machine learning workflow, including data collection, data preparation,
modeling, deployment and management of predictive services in production
environments.
An Excessive Fascination with the Apache Brand
PredictionIO is already a widely known open source project. This proposal
is not for the purpose of generating publicity. Rather, the primary
benefits to joining Apache are those outlined in the Rationale section.
Documentation
PredictionIO boasts rich and live documentation, included in the code repo
(docs/manual directory), is built with Middleman, and publicly hosted at
https://docs.prediction.io
Initial Source and Intellectual Property Submission Plan
Currently, the PredictionIO codebase is distributed under the Apache 2.0
License and hosted on GitHub: https://github.com/PredictionIO/PredictionIO
External Dependencies
PredictionIO has the following external dependencies:
* Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are needed)
* Apache Spark 1.3.0 for Hadoop 2.4
* Java SE Development Kit 8
* and one of the following sets:
* PostgreSQL 9.1
or
* MySQL 5.1
or
* Apache HBase 0.98.6
* Elasticsearch 1.4.0
Upon acceptance to the incubator, we would begin a thorough analysis of
all transitive dependencies to verify this information and introduce
license checking into the build and release process by integrating with
Apache RAT.
Cryptography
PredictionIO does not include cryptographic code. We utilize standard
JCE and JSSE APIs provided by the Java Runtime Environment.
Required Resources
We request that following resources be created for the project to use
Mailing lists
predictionio-private@incubator.apache.org (with moderated subscriptions)
predictionio-dev
predictionio-user
predictionio-commits
We will migrate the existing PredictionIO mailing lists.
Git repository
The PredictionIO team would like to use Git for source control, due to our
current use of GitHub.
git://git.apache.org/incubator-predictionio
Documentation
https://predictionio.incubator.apache.org/docs/
JIRA instance
PredictionIO currently uses the GitHub issue tracking system associated
with its repository: https://github.com/PredictionIO/PredictionIO/issues.
We will migrate to Apache JIRA.
JIRA PREDICTIONIO
https://issues.apache.org/jira/browse/PREDICTIONIO
Other Resources
TravisCI for builds and test running.
PredictionIO's documentation, included in the code repo (docs/manual
directory), is built with Middleman and publicly hosted at
https://docs.prediction.io
A blog to drive adoption and excitement at https://blog.prediction.io
Initial Committers
Pat Ferrell
Tamas Jambor
Justin Yip
Xusen Yin
Lee Moon Soo
Donald Szeto
Kenneth Chan
Tom Chan
Simon Chan
Marco Vivero
Matthew Tovbin
Yevgeny Khodorkovsky
Felipe Oliveira
Vitaly Gordon
Alex Merritt
Affiliations
Pat Ferrell - ActionML
Tamas Jambor - Channel4
Justin Yip - independent
Xusen Yin - USC
Lee Moon Soo - NFLabs
Donald Szeto - Salesforce
Kenneth Chan - Salesforce
Tom Chan - Salesforce
Simon Chan - Salesforce
Marco Vivero - Salesforce
Matthew Tovbin - Salesforce
Yevgeny Khodorkovsky - Salesforce
Felipe Oliveira - Salesforce
Vitaly Gordon - Salesforce
Alex Merritt - ActionML
Sponsors
Champion
Andrew Purtell <apurtell at apache dot org>
Nominated Mentors
Andrew Purtell <apurtell at apache dot org>
James Taylor <jtaylor at apache dot org>
Lars Hofhansl <larsh at apache dot org>
Suneel Marthi <smarthi at apache dot org>
Xiangrui Meng <meng at apache dot org>
Luciano Resende <lresende at apache dot org>
Sponsoring Entity
Apache Incubator PMC
--
Best regards,
- Andy
Problems worthy of attack prove their worth by hitting back. - Piet Hein
(via Tom White)
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by "Gangumalla, Uma" <um...@intel.com>.
+1 (binding)
Regards,
Uma
On 5/23/16, 3:22 PM, "Andrew Purtell" <ap...@apache.org> wrote:
>Since discussion on the matter of PredictionIO has died down, I would like
>to call a VOTE
>on accepting PredictionIO into the Apache Incubator.
>
>Proposal: https://wiki.apache.org/incubator/PredictionIO
>
>[ ] +1 Accept PredictionIO into the Apache Incubator
>[ ] +0 Abstain
>[ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
>
>This vote will be open for at least 72 hours.
>
>My vote is +1 (binding)
>
>--
>
>PredictionIO Proposal
>
>Abstract
>
>PredictionIO is an open source Machine Learning Server built on top of
>state-of-the-art open source stack, that enables developers to manage and
>deploy production-ready predictive services for various kinds of machine
>learning tasks.
>
>Proposal
>
>The PredictionIO platform consists of the following components:
>
> * PredictionIO framework - provides the machine learning stack for
> building, evaluating and deploying engines with machine learning
> algorithms. It uses Apache Spark for processing.
>
> * Event Server - the machine learning analytics layer for unifying
>events
> from multiple platforms. It can use Apache HBase or any JDBC backends
> as its data store.
>
>The PredictionIO community also maintains a Template Gallery, a place to
>publish and download (free or proprietary) engine templates for different
>types of machine learning applications, and is a complemental part of the
>project. At this point we exclude the Template Gallery from the proposal,
>as it has a separate set of contributors and we’re not familiar with an
>Apache approved mechanism to maintain such a gallery.
>
>Background
>
>PredictionIO was started with a mission to democratize and bring machine
>learning to the masses.
>
>Machine learning has traditionally been a luxury for big companies like
>Google, Facebook, and Netflix. There are ML libraries and tools lying
>around the internet but the effort of putting them all together as a
>production-ready infrastructure is a very resource-intensive task that is
>remotely reachable by individuals or small businesses.
>
>PredictionIO is a production-ready, full stack machine learning system
>that
>allows organizations of any scale to quickly deploy machine learning
>capabilities. It comes with official and community-contributed machine
>learning engine templates that are easy to customize.
>
>Rationale
>
>As usage and number of contributors to PredictionIO has grown bigger and
>more diverse, we have sought for an independent framework for the project
>to keep thriving. We believe the Apache foundation is a great fit. Joining
>Apache would ensure that tried and true processes and procedures are in
>place for the growing number of organizations interested in contributing
>to PredictionIO. PredictionIO is also a good fit for the Apache
>foundation.
>PredictionIO was built on top of several Apache projects (HBase, Spark,
>Hadoop). We are familiar with the Apache process and believe that the
>democratic and meritocratic nature of the foundation aligns with the
>project goals.
>
>Initial Goals
>
>The initial milestones will be to move the existing codebase to Apache and
>integrate with the Apache development process. Once this is accomplished,
>we plan for incremental development and releases that follow the Apache
>guidelines, as well as growing our developer and user communities.
>
>Current Status
>
>PredictionIO has undergone nine minor releases and many patches.
>PredictionIO is being used in production by Salesforce.com as well as many
>other organizations and apps. The PredictionIO codebase is currently
>hosted at GitHub, which will form the basis of the Apache git repository.
>
>Meritocracy
>
>We plan to invest in supporting a meritocracy. We will discuss the
>requirements in an open forum. We intend to invite additional developers
>to participate. We will encourage and monitor community participation so
>that privileges can be extended to those that contribute.
>
>Community
>
>Acceptance into the Apache foundation would bolster the already strong
>user and developer community around PredictionIO. That community includes
>many contributors from various other companies, and an active mailing list
>composed of hundreds of users.
>
>Core Developers
>
>The core developers of our project are listed in our contributors and
>initial PPMC below. Though many are employed at Salesforce.com, there are
>also engineers from ActionML, and independent developers.
>
>Alignment
>
>The ASF is the natural choice to host the PredictionIO project as its goal
>is democratizing Machine Learning by making it more easily accessible to
>every user/developer. PredictionIO is built on top of several top level
>Apache projects as outlined above.
>
>Known Risks
>
>Orphaned Products
>
>PredictionIO has a solid and growing community. It is deployed on
>production environments by companies of all sizes to run various kinds of
>predictive engines.
>
>In addition to the community contribution to PredictionIO framework, the
>community is also actively contributing new engines to the Template
>Gallery as well as SDKs and documentation for the project. Salesforce is
>committed to utilize and advance the PredictionIO code base and support
>its user community.
>
>Inexperience with Open Source
>
>PredictionIO has existed as a healthy open source project for almost two
>years and is the most starred Scala project on GitHub. All of the proposed
>committers have contributed to ASF and Linux Foundation open source
>projects. Several current committers on Apache projects and Apache Members
>are involved in this proposal and intend to provide mentorship.
>
>Homogeneous Developers
>
>The initial list of committers includes developers from several
>institutions, including Salesforce, ActionML, Channel4, USC as well as
>unaffiliated developers.
>
>Reliance on Salaried Developers
>
>Like most open source projects, PredictionIO receives substantial support
>from salaried developers. PredictionIO development is partially supported
>by Salesforce.com, but there are many contributors from various other
>companies, and an active mailing list composed of hundreds of users. We
>will continue our efforts to ensure stewardship of the project to be
>independent of salaried developers by meritocratically promoting those
>contributors to committers.
>
>Relationships with Other Apache Product
>
>PredictionIO relies heavily on top level Apache projects such as Apache
>Spark, HBase and Hadoop. However it brings a distinguished functionality,
>rather than just an abstraction - Machine Learning in a plug-and-play
>fashion.
>
>Compared to Apache Mahout, which focuses on the development of a wide
>variety of algorithms, PredictionIO offers a platform to manage the whole
>machine learning workflow, including data collection, data preparation,
>modeling, deployment and management of predictive services in production
>environments.
>
>An Excessive Fascination with the Apache Brand
>
>PredictionIO is already a widely known open source project. This proposal
>is not for the purpose of generating publicity. Rather, the primary
>benefits to joining Apache are those outlined in the Rationale section.
>
>Documentation
>
>PredictionIO boasts rich and live documentation, included in the code repo
>(docs/manual directory), is built with Middleman, and publicly hosted at
>https://docs.prediction.io
>
>Initial Source and Intellectual Property Submission Plan
>
>Currently, the PredictionIO codebase is distributed under the Apache 2.0
>License and hosted on GitHub: https://github.com/PredictionIO/PredictionIO
>
>External Dependencies
>
>PredictionIO has the following external dependencies:
> * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are
>needed)
> * Apache Spark 1.3.0 for Hadoop 2.4
> * Java SE Development Kit 8
> * and one of the following sets:
> * PostgreSQL 9.1
> or
> * MySQL 5.1
> or
> * Apache HBase 0.98.6
> * Elasticsearch 1.4.0
>
>Upon acceptance to the incubator, we would begin a thorough analysis of
>all transitive dependencies to verify this information and introduce
>license checking into the build and release process by integrating with
>Apache RAT.
>
>Cryptography
>
>PredictionIO does not include cryptographic code. We utilize standard
>JCE and JSSE APIs provided by the Java Runtime Environment.
>
>Required Resources
>
>We request that following resources be created for the project to use
>
>Mailing lists
>
> predictionio-private@incubator.apache.org (with moderated subscriptions)
> predictionio-dev
> predictionio-user
> predictionio-commits
>
> We will migrate the existing PredictionIO mailing lists.
>
>Git repository
>
> The PredictionIO team would like to use Git for source control, due to
>our
> current use of GitHub.
>
> git://git.apache.org/incubator-predictionio
>
>Documentation
>
> https://predictionio.incubator.apache.org/docs/
>
>JIRA instance
>
> PredictionIO currently uses the GitHub issue tracking system associated
> with its repository:
>https://github.com/PredictionIO/PredictionIO/issues.
> We will migrate to Apache JIRA.
>
> JIRA PREDICTIONIO
> https://issues.apache.org/jira/browse/PREDICTIONIO
>
>Other Resources
>
> TravisCI for builds and test running.
>
> PredictionIO's documentation, included in the code repo (docs/manual
> directory), is built with Middleman and publicly hosted at
> https://docs.prediction.io
>
> A blog to drive adoption and excitement at https://blog.prediction.io
>
>Initial Committers
>
> Pat Ferrell
> Tamas Jambor
> Justin Yip
> Xusen Yin
> Lee Moon Soo
> Donald Szeto
> Kenneth Chan
> Tom Chan
> Simon Chan
> Marco Vivero
> Matthew Tovbin
> Yevgeny Khodorkovsky
> Felipe Oliveira
> Vitaly Gordon
> Alex Merritt
>
>Affiliations
>
> Pat Ferrell - ActionML
> Tamas Jambor - Channel4
> Justin Yip - independent
> Xusen Yin - USC
> Lee Moon Soo - NFLabs
> Donald Szeto - Salesforce
> Kenneth Chan - Salesforce
> Tom Chan - Salesforce
> Simon Chan - Salesforce
> Marco Vivero - Salesforce
> Matthew Tovbin - Salesforce
> Yevgeny Khodorkovsky - Salesforce
> Felipe Oliveira - Salesforce
> Vitaly Gordon - Salesforce
> Alex Merritt - ActionML
>
>Sponsors
>
>Champion
>
> Andrew Purtell <apurtell at apache dot org>
>
>Nominated Mentors
>
> Andrew Purtell <apurtell at apache dot org>
> James Taylor <jtaylor at apache dot org>
> Lars Hofhansl <larsh at apache dot org>
> Suneel Marthi <smarthi at apache dot org>
> Xiangrui Meng <meng at apache dot org>
> Luciano Resende <lresende at apache dot org>
>
>Sponsoring Entity
>
> Apache Incubator PMC
>
>
>--
>Best regards,
>
> - Andy
>
>Problems worthy of attack prove their worth by hitting back. - Piet Hein
>(via Tom White)
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Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Seetharam Venkatesh <ve...@innerzeal.com>.
+1 (binding)
All the best,
Venkatesh
On Tue, May 24, 2016 at 7:15 AM Ralph Goers <ra...@dslextreme.com>
wrote:
> +1 (binding)
>
> Ralph
>
> > On May 24, 2016, at 3:44 AM, John D. Ament <jo...@apache.org>
> wrote:
> >
> > +1
> >
> > On Mon, May 23, 2016 at 6:23 PM Andrew Purtell <ap...@apache.org>
> wrote:
> >
> >> Since discussion on the matter of PredictionIO has died down, I would
> like
> >> to call a VOTE
> >> on accepting PredictionIO into the Apache Incubator.
> >>
> >> Proposal: https://wiki.apache.org/incubator/PredictionIO
> >>
> >> [ ] +1 Accept PredictionIO into the Apache Incubator
> >> [ ] +0 Abstain
> >> [ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
> >>
> >> This vote will be open for at least 72 hours.
> >>
> >> My vote is +1 (binding)
> >>
> >> --
> >>
> >> PredictionIO Proposal
> >>
> >> Abstract
> >>
> >> PredictionIO is an open source Machine Learning Server built on top of
> >> state-of-the-art open source stack, that enables developers to manage
> and
> >> deploy production-ready predictive services for various kinds of machine
> >> learning tasks.
> >>
> >> Proposal
> >>
> >> The PredictionIO platform consists of the following components:
> >>
> >> * PredictionIO framework - provides the machine learning stack for
> >> building, evaluating and deploying engines with machine learning
> >> algorithms. It uses Apache Spark for processing.
> >>
> >> * Event Server - the machine learning analytics layer for unifying
> >> events
> >> from multiple platforms. It can use Apache HBase or any JDBC
> backends
> >> as its data store.
> >>
> >> The PredictionIO community also maintains a Template Gallery, a place to
> >> publish and download (free or proprietary) engine templates for
> different
> >> types of machine learning applications, and is a complemental part of
> the
> >> project. At this point we exclude the Template Gallery from the
> proposal,
> >> as it has a separate set of contributors and we’re not familiar with an
> >> Apache approved mechanism to maintain such a gallery.
> >>
> >> Background
> >>
> >> PredictionIO was started with a mission to democratize and bring machine
> >> learning to the masses.
> >>
> >> Machine learning has traditionally been a luxury for big companies like
> >> Google, Facebook, and Netflix. There are ML libraries and tools lying
> >> around the internet but the effort of putting them all together as a
> >> production-ready infrastructure is a very resource-intensive task that
> is
> >> remotely reachable by individuals or small businesses.
> >>
> >> PredictionIO is a production-ready, full stack machine learning system
> that
> >> allows organizations of any scale to quickly deploy machine learning
> >> capabilities. It comes with official and community-contributed machine
> >> learning engine templates that are easy to customize.
> >>
> >> Rationale
> >>
> >> As usage and number of contributors to PredictionIO has grown bigger and
> >> more diverse, we have sought for an independent framework for the
> project
> >> to keep thriving. We believe the Apache foundation is a great fit.
> Joining
> >> Apache would ensure that tried and true processes and procedures are in
> >> place for the growing number of organizations interested in contributing
> >> to PredictionIO. PredictionIO is also a good fit for the Apache
> foundation.
> >> PredictionIO was built on top of several Apache projects (HBase, Spark,
> >> Hadoop). We are familiar with the Apache process and believe that the
> >> democratic and meritocratic nature of the foundation aligns with the
> >> project goals.
> >>
> >> Initial Goals
> >>
> >> The initial milestones will be to move the existing codebase to Apache
> and
> >> integrate with the Apache development process. Once this is
> accomplished,
> >> we plan for incremental development and releases that follow the Apache
> >> guidelines, as well as growing our developer and user communities.
> >>
> >> Current Status
> >>
> >> PredictionIO has undergone nine minor releases and many patches.
> >> PredictionIO is being used in production by Salesforce.com as well as
> many
> >> other organizations and apps. The PredictionIO codebase is currently
> >> hosted at GitHub, which will form the basis of the Apache git
> repository.
> >>
> >> Meritocracy
> >>
> >> We plan to invest in supporting a meritocracy. We will discuss the
> >> requirements in an open forum. We intend to invite additional developers
> >> to participate. We will encourage and monitor community participation so
> >> that privileges can be extended to those that contribute.
> >>
> >> Community
> >>
> >> Acceptance into the Apache foundation would bolster the already strong
> >> user and developer community around PredictionIO. That community
> includes
> >> many contributors from various other companies, and an active mailing
> list
> >> composed of hundreds of users.
> >>
> >> Core Developers
> >>
> >> The core developers of our project are listed in our contributors and
> >> initial PPMC below. Though many are employed at Salesforce.com, there
> are
> >> also engineers from ActionML, and independent developers.
> >>
> >> Alignment
> >>
> >> The ASF is the natural choice to host the PredictionIO project as its
> goal
> >> is democratizing Machine Learning by making it more easily accessible to
> >> every user/developer. PredictionIO is built on top of several top level
> >> Apache projects as outlined above.
> >>
> >> Known Risks
> >>
> >> Orphaned Products
> >>
> >> PredictionIO has a solid and growing community. It is deployed on
> >> production environments by companies of all sizes to run various kinds
> of
> >> predictive engines.
> >>
> >> In addition to the community contribution to PredictionIO framework, the
> >> community is also actively contributing new engines to the Template
> >> Gallery as well as SDKs and documentation for the project. Salesforce is
> >> committed to utilize and advance the PredictionIO code base and support
> >> its user community.
> >>
> >> Inexperience with Open Source
> >>
> >> PredictionIO has existed as a healthy open source project for almost two
> >> years and is the most starred Scala project on GitHub. All of the
> proposed
> >> committers have contributed to ASF and Linux Foundation open source
> >> projects. Several current committers on Apache projects and Apache
> Members
> >> are involved in this proposal and intend to provide mentorship.
> >>
> >> Homogeneous Developers
> >>
> >> The initial list of committers includes developers from several
> >> institutions, including Salesforce, ActionML, Channel4, USC as well as
> >> unaffiliated developers.
> >>
> >> Reliance on Salaried Developers
> >>
> >> Like most open source projects, PredictionIO receives substantial
> support
> >> from salaried developers. PredictionIO development is partially
> supported
> >> by Salesforce.com, but there are many contributors from various other
> >> companies, and an active mailing list composed of hundreds of users. We
> >> will continue our efforts to ensure stewardship of the project to be
> >> independent of salaried developers by meritocratically promoting those
> >> contributors to committers.
> >>
> >> Relationships with Other Apache Product
> >>
> >> PredictionIO relies heavily on top level Apache projects such as Apache
> >> Spark, HBase and Hadoop. However it brings a distinguished
> functionality,
> >> rather than just an abstraction - Machine Learning in a plug-and-play
> >> fashion.
> >>
> >> Compared to Apache Mahout, which focuses on the development of a wide
> >> variety of algorithms, PredictionIO offers a platform to manage the
> whole
> >> machine learning workflow, including data collection, data preparation,
> >> modeling, deployment and management of predictive services in production
> >> environments.
> >>
> >> An Excessive Fascination with the Apache Brand
> >>
> >> PredictionIO is already a widely known open source project. This
> proposal
> >> is not for the purpose of generating publicity. Rather, the primary
> >> benefits to joining Apache are those outlined in the Rationale section.
> >>
> >> Documentation
> >>
> >> PredictionIO boasts rich and live documentation, included in the code
> repo
> >> (docs/manual directory), is built with Middleman, and publicly hosted at
> >> https://docs.prediction.io
> >>
> >> Initial Source and Intellectual Property Submission Plan
> >>
> >> Currently, the PredictionIO codebase is distributed under the Apache 2.0
> >> License and hosted on GitHub:
> https://github.com/PredictionIO/PredictionIO
> >>
> >> External Dependencies
> >>
> >> PredictionIO has the following external dependencies:
> >> * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are
> >> needed)
> >> * Apache Spark 1.3.0 for Hadoop 2.4
> >> * Java SE Development Kit 8
> >> * and one of the following sets:
> >> * PostgreSQL 9.1
> >> or
> >> * MySQL 5.1
> >> or
> >> * Apache HBase 0.98.6
> >> * Elasticsearch 1.4.0
> >>
> >> Upon acceptance to the incubator, we would begin a thorough analysis of
> >> all transitive dependencies to verify this information and introduce
> >> license checking into the build and release process by integrating with
> >> Apache RAT.
> >>
> >> Cryptography
> >>
> >> PredictionIO does not include cryptographic code. We utilize standard
> >> JCE and JSSE APIs provided by the Java Runtime Environment.
> >>
> >> Required Resources
> >>
> >> We request that following resources be created for the project to use
> >>
> >> Mailing lists
> >>
> >> predictionio-private@incubator.apache.org (with moderated
> subscriptions)
> >> predictionio-dev
> >> predictionio-user
> >> predictionio-commits
> >>
> >> We will migrate the existing PredictionIO mailing lists.
> >>
> >> Git repository
> >>
> >> The PredictionIO team would like to use Git for source control, due to
> >> our
> >> current use of GitHub.
> >>
> >> git://git.apache.org/incubator-predictionio
> >>
> >> Documentation
> >>
> >> https://predictionio.incubator.apache.org/docs/
> >>
> >> JIRA instance
> >>
> >> PredictionIO currently uses the GitHub issue tracking system associated
> >> with its repository:
> https://github.com/PredictionIO/PredictionIO/issues
> >> .
> >> We will migrate to Apache JIRA.
> >>
> >> JIRA PREDICTIONIO
> >> https://issues.apache.org/jira/browse/PREDICTIONIO
> >>
> >> Other Resources
> >>
> >> TravisCI for builds and test running.
> >>
> >> PredictionIO's documentation, included in the code repo (docs/manual
> >> directory), is built with Middleman and publicly hosted at
> >> https://docs.prediction.io
> >>
> >> A blog to drive adoption and excitement at https://blog.prediction.io
> >>
> >> Initial Committers
> >>
> >> Pat Ferrell
> >> Tamas Jambor
> >> Justin Yip
> >> Xusen Yin
> >> Lee Moon Soo
> >> Donald Szeto
> >> Kenneth Chan
> >> Tom Chan
> >> Simon Chan
> >> Marco Vivero
> >> Matthew Tovbin
> >> Yevgeny Khodorkovsky
> >> Felipe Oliveira
> >> Vitaly Gordon
> >> Alex Merritt
> >>
> >> Affiliations
> >>
> >> Pat Ferrell - ActionML
> >> Tamas Jambor - Channel4
> >> Justin Yip - independent
> >> Xusen Yin - USC
> >> Lee Moon Soo - NFLabs
> >> Donald Szeto - Salesforce
> >> Kenneth Chan - Salesforce
> >> Tom Chan - Salesforce
> >> Simon Chan - Salesforce
> >> Marco Vivero - Salesforce
> >> Matthew Tovbin - Salesforce
> >> Yevgeny Khodorkovsky - Salesforce
> >> Felipe Oliveira - Salesforce
> >> Vitaly Gordon - Salesforce
> >> Alex Merritt - ActionML
> >>
> >> Sponsors
> >>
> >> Champion
> >>
> >> Andrew Purtell <apurtell at apache dot org>
> >>
> >> Nominated Mentors
> >>
> >> Andrew Purtell <apurtell at apache dot org>
> >> James Taylor <jtaylor at apache dot org>
> >> Lars Hofhansl <larsh at apache dot org>
> >> Suneel Marthi <smarthi at apache dot org>
> >> Xiangrui Meng <meng at apache dot org>
> >> Luciano Resende <lresende at apache dot org>
> >>
> >> Sponsoring Entity
> >>
> >> Apache Incubator PMC
> >>
> >>
> >> --
> >> Best regards,
> >>
> >> - Andy
> >>
> >> Problems worthy of attack prove their worth by hitting back. - Piet Hein
> >> (via Tom White)
> >>
>
>
>
> ---------------------------------------------------------------------
> To unsubscribe, e-mail: general-unsubscribe@incubator.apache.org
> For additional commands, e-mail: general-help@incubator.apache.org
>
>
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Ralph Goers <ra...@dslextreme.com>.
+1 (binding)
Ralph
> On May 24, 2016, at 3:44 AM, John D. Ament <jo...@apache.org> wrote:
>
> +1
>
> On Mon, May 23, 2016 at 6:23 PM Andrew Purtell <ap...@apache.org> wrote:
>
>> Since discussion on the matter of PredictionIO has died down, I would like
>> to call a VOTE
>> on accepting PredictionIO into the Apache Incubator.
>>
>> Proposal: https://wiki.apache.org/incubator/PredictionIO
>>
>> [ ] +1 Accept PredictionIO into the Apache Incubator
>> [ ] +0 Abstain
>> [ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
>>
>> This vote will be open for at least 72 hours.
>>
>> My vote is +1 (binding)
>>
>> --
>>
>> PredictionIO Proposal
>>
>> Abstract
>>
>> PredictionIO is an open source Machine Learning Server built on top of
>> state-of-the-art open source stack, that enables developers to manage and
>> deploy production-ready predictive services for various kinds of machine
>> learning tasks.
>>
>> Proposal
>>
>> The PredictionIO platform consists of the following components:
>>
>> * PredictionIO framework - provides the machine learning stack for
>> building, evaluating and deploying engines with machine learning
>> algorithms. It uses Apache Spark for processing.
>>
>> * Event Server - the machine learning analytics layer for unifying
>> events
>> from multiple platforms. It can use Apache HBase or any JDBC backends
>> as its data store.
>>
>> The PredictionIO community also maintains a Template Gallery, a place to
>> publish and download (free or proprietary) engine templates for different
>> types of machine learning applications, and is a complemental part of the
>> project. At this point we exclude the Template Gallery from the proposal,
>> as it has a separate set of contributors and we’re not familiar with an
>> Apache approved mechanism to maintain such a gallery.
>>
>> Background
>>
>> PredictionIO was started with a mission to democratize and bring machine
>> learning to the masses.
>>
>> Machine learning has traditionally been a luxury for big companies like
>> Google, Facebook, and Netflix. There are ML libraries and tools lying
>> around the internet but the effort of putting them all together as a
>> production-ready infrastructure is a very resource-intensive task that is
>> remotely reachable by individuals or small businesses.
>>
>> PredictionIO is a production-ready, full stack machine learning system that
>> allows organizations of any scale to quickly deploy machine learning
>> capabilities. It comes with official and community-contributed machine
>> learning engine templates that are easy to customize.
>>
>> Rationale
>>
>> As usage and number of contributors to PredictionIO has grown bigger and
>> more diverse, we have sought for an independent framework for the project
>> to keep thriving. We believe the Apache foundation is a great fit. Joining
>> Apache would ensure that tried and true processes and procedures are in
>> place for the growing number of organizations interested in contributing
>> to PredictionIO. PredictionIO is also a good fit for the Apache foundation.
>> PredictionIO was built on top of several Apache projects (HBase, Spark,
>> Hadoop). We are familiar with the Apache process and believe that the
>> democratic and meritocratic nature of the foundation aligns with the
>> project goals.
>>
>> Initial Goals
>>
>> The initial milestones will be to move the existing codebase to Apache and
>> integrate with the Apache development process. Once this is accomplished,
>> we plan for incremental development and releases that follow the Apache
>> guidelines, as well as growing our developer and user communities.
>>
>> Current Status
>>
>> PredictionIO has undergone nine minor releases and many patches.
>> PredictionIO is being used in production by Salesforce.com as well as many
>> other organizations and apps. The PredictionIO codebase is currently
>> hosted at GitHub, which will form the basis of the Apache git repository.
>>
>> Meritocracy
>>
>> We plan to invest in supporting a meritocracy. We will discuss the
>> requirements in an open forum. We intend to invite additional developers
>> to participate. We will encourage and monitor community participation so
>> that privileges can be extended to those that contribute.
>>
>> Community
>>
>> Acceptance into the Apache foundation would bolster the already strong
>> user and developer community around PredictionIO. That community includes
>> many contributors from various other companies, and an active mailing list
>> composed of hundreds of users.
>>
>> Core Developers
>>
>> The core developers of our project are listed in our contributors and
>> initial PPMC below. Though many are employed at Salesforce.com, there are
>> also engineers from ActionML, and independent developers.
>>
>> Alignment
>>
>> The ASF is the natural choice to host the PredictionIO project as its goal
>> is democratizing Machine Learning by making it more easily accessible to
>> every user/developer. PredictionIO is built on top of several top level
>> Apache projects as outlined above.
>>
>> Known Risks
>>
>> Orphaned Products
>>
>> PredictionIO has a solid and growing community. It is deployed on
>> production environments by companies of all sizes to run various kinds of
>> predictive engines.
>>
>> In addition to the community contribution to PredictionIO framework, the
>> community is also actively contributing new engines to the Template
>> Gallery as well as SDKs and documentation for the project. Salesforce is
>> committed to utilize and advance the PredictionIO code base and support
>> its user community.
>>
>> Inexperience with Open Source
>>
>> PredictionIO has existed as a healthy open source project for almost two
>> years and is the most starred Scala project on GitHub. All of the proposed
>> committers have contributed to ASF and Linux Foundation open source
>> projects. Several current committers on Apache projects and Apache Members
>> are involved in this proposal and intend to provide mentorship.
>>
>> Homogeneous Developers
>>
>> The initial list of committers includes developers from several
>> institutions, including Salesforce, ActionML, Channel4, USC as well as
>> unaffiliated developers.
>>
>> Reliance on Salaried Developers
>>
>> Like most open source projects, PredictionIO receives substantial support
>> from salaried developers. PredictionIO development is partially supported
>> by Salesforce.com, but there are many contributors from various other
>> companies, and an active mailing list composed of hundreds of users. We
>> will continue our efforts to ensure stewardship of the project to be
>> independent of salaried developers by meritocratically promoting those
>> contributors to committers.
>>
>> Relationships with Other Apache Product
>>
>> PredictionIO relies heavily on top level Apache projects such as Apache
>> Spark, HBase and Hadoop. However it brings a distinguished functionality,
>> rather than just an abstraction - Machine Learning in a plug-and-play
>> fashion.
>>
>> Compared to Apache Mahout, which focuses on the development of a wide
>> variety of algorithms, PredictionIO offers a platform to manage the whole
>> machine learning workflow, including data collection, data preparation,
>> modeling, deployment and management of predictive services in production
>> environments.
>>
>> An Excessive Fascination with the Apache Brand
>>
>> PredictionIO is already a widely known open source project. This proposal
>> is not for the purpose of generating publicity. Rather, the primary
>> benefits to joining Apache are those outlined in the Rationale section.
>>
>> Documentation
>>
>> PredictionIO boasts rich and live documentation, included in the code repo
>> (docs/manual directory), is built with Middleman, and publicly hosted at
>> https://docs.prediction.io
>>
>> Initial Source and Intellectual Property Submission Plan
>>
>> Currently, the PredictionIO codebase is distributed under the Apache 2.0
>> License and hosted on GitHub: https://github.com/PredictionIO/PredictionIO
>>
>> External Dependencies
>>
>> PredictionIO has the following external dependencies:
>> * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are
>> needed)
>> * Apache Spark 1.3.0 for Hadoop 2.4
>> * Java SE Development Kit 8
>> * and one of the following sets:
>> * PostgreSQL 9.1
>> or
>> * MySQL 5.1
>> or
>> * Apache HBase 0.98.6
>> * Elasticsearch 1.4.0
>>
>> Upon acceptance to the incubator, we would begin a thorough analysis of
>> all transitive dependencies to verify this information and introduce
>> license checking into the build and release process by integrating with
>> Apache RAT.
>>
>> Cryptography
>>
>> PredictionIO does not include cryptographic code. We utilize standard
>> JCE and JSSE APIs provided by the Java Runtime Environment.
>>
>> Required Resources
>>
>> We request that following resources be created for the project to use
>>
>> Mailing lists
>>
>> predictionio-private@incubator.apache.org (with moderated subscriptions)
>> predictionio-dev
>> predictionio-user
>> predictionio-commits
>>
>> We will migrate the existing PredictionIO mailing lists.
>>
>> Git repository
>>
>> The PredictionIO team would like to use Git for source control, due to
>> our
>> current use of GitHub.
>>
>> git://git.apache.org/incubator-predictionio
>>
>> Documentation
>>
>> https://predictionio.incubator.apache.org/docs/
>>
>> JIRA instance
>>
>> PredictionIO currently uses the GitHub issue tracking system associated
>> with its repository: https://github.com/PredictionIO/PredictionIO/issues
>> .
>> We will migrate to Apache JIRA.
>>
>> JIRA PREDICTIONIO
>> https://issues.apache.org/jira/browse/PREDICTIONIO
>>
>> Other Resources
>>
>> TravisCI for builds and test running.
>>
>> PredictionIO's documentation, included in the code repo (docs/manual
>> directory), is built with Middleman and publicly hosted at
>> https://docs.prediction.io
>>
>> A blog to drive adoption and excitement at https://blog.prediction.io
>>
>> Initial Committers
>>
>> Pat Ferrell
>> Tamas Jambor
>> Justin Yip
>> Xusen Yin
>> Lee Moon Soo
>> Donald Szeto
>> Kenneth Chan
>> Tom Chan
>> Simon Chan
>> Marco Vivero
>> Matthew Tovbin
>> Yevgeny Khodorkovsky
>> Felipe Oliveira
>> Vitaly Gordon
>> Alex Merritt
>>
>> Affiliations
>>
>> Pat Ferrell - ActionML
>> Tamas Jambor - Channel4
>> Justin Yip - independent
>> Xusen Yin - USC
>> Lee Moon Soo - NFLabs
>> Donald Szeto - Salesforce
>> Kenneth Chan - Salesforce
>> Tom Chan - Salesforce
>> Simon Chan - Salesforce
>> Marco Vivero - Salesforce
>> Matthew Tovbin - Salesforce
>> Yevgeny Khodorkovsky - Salesforce
>> Felipe Oliveira - Salesforce
>> Vitaly Gordon - Salesforce
>> Alex Merritt - ActionML
>>
>> Sponsors
>>
>> Champion
>>
>> Andrew Purtell <apurtell at apache dot org>
>>
>> Nominated Mentors
>>
>> Andrew Purtell <apurtell at apache dot org>
>> James Taylor <jtaylor at apache dot org>
>> Lars Hofhansl <larsh at apache dot org>
>> Suneel Marthi <smarthi at apache dot org>
>> Xiangrui Meng <meng at apache dot org>
>> Luciano Resende <lresende at apache dot org>
>>
>> Sponsoring Entity
>>
>> Apache Incubator PMC
>>
>>
>> --
>> Best regards,
>>
>> - Andy
>>
>> Problems worthy of attack prove their worth by hitting back. - Piet Hein
>> (via Tom White)
>>
---------------------------------------------------------------------
To unsubscribe, e-mail: general-unsubscribe@incubator.apache.org
For additional commands, e-mail: general-help@incubator.apache.org
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by "John D. Ament" <jo...@apache.org>.
+1
On Mon, May 23, 2016 at 6:23 PM Andrew Purtell <ap...@apache.org> wrote:
> Since discussion on the matter of PredictionIO has died down, I would like
> to call a VOTE
> on accepting PredictionIO into the Apache Incubator.
>
> Proposal: https://wiki.apache.org/incubator/PredictionIO
>
> [ ] +1 Accept PredictionIO into the Apache Incubator
> [ ] +0 Abstain
> [ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
>
> This vote will be open for at least 72 hours.
>
> My vote is +1 (binding)
>
> --
>
> PredictionIO Proposal
>
> Abstract
>
> PredictionIO is an open source Machine Learning Server built on top of
> state-of-the-art open source stack, that enables developers to manage and
> deploy production-ready predictive services for various kinds of machine
> learning tasks.
>
> Proposal
>
> The PredictionIO platform consists of the following components:
>
> * PredictionIO framework - provides the machine learning stack for
> building, evaluating and deploying engines with machine learning
> algorithms. It uses Apache Spark for processing.
>
> * Event Server - the machine learning analytics layer for unifying
> events
> from multiple platforms. It can use Apache HBase or any JDBC backends
> as its data store.
>
> The PredictionIO community also maintains a Template Gallery, a place to
> publish and download (free or proprietary) engine templates for different
> types of machine learning applications, and is a complemental part of the
> project. At this point we exclude the Template Gallery from the proposal,
> as it has a separate set of contributors and we’re not familiar with an
> Apache approved mechanism to maintain such a gallery.
>
> Background
>
> PredictionIO was started with a mission to democratize and bring machine
> learning to the masses.
>
> Machine learning has traditionally been a luxury for big companies like
> Google, Facebook, and Netflix. There are ML libraries and tools lying
> around the internet but the effort of putting them all together as a
> production-ready infrastructure is a very resource-intensive task that is
> remotely reachable by individuals or small businesses.
>
> PredictionIO is a production-ready, full stack machine learning system that
> allows organizations of any scale to quickly deploy machine learning
> capabilities. It comes with official and community-contributed machine
> learning engine templates that are easy to customize.
>
> Rationale
>
> As usage and number of contributors to PredictionIO has grown bigger and
> more diverse, we have sought for an independent framework for the project
> to keep thriving. We believe the Apache foundation is a great fit. Joining
> Apache would ensure that tried and true processes and procedures are in
> place for the growing number of organizations interested in contributing
> to PredictionIO. PredictionIO is also a good fit for the Apache foundation.
> PredictionIO was built on top of several Apache projects (HBase, Spark,
> Hadoop). We are familiar with the Apache process and believe that the
> democratic and meritocratic nature of the foundation aligns with the
> project goals.
>
> Initial Goals
>
> The initial milestones will be to move the existing codebase to Apache and
> integrate with the Apache development process. Once this is accomplished,
> we plan for incremental development and releases that follow the Apache
> guidelines, as well as growing our developer and user communities.
>
> Current Status
>
> PredictionIO has undergone nine minor releases and many patches.
> PredictionIO is being used in production by Salesforce.com as well as many
> other organizations and apps. The PredictionIO codebase is currently
> hosted at GitHub, which will form the basis of the Apache git repository.
>
> Meritocracy
>
> We plan to invest in supporting a meritocracy. We will discuss the
> requirements in an open forum. We intend to invite additional developers
> to participate. We will encourage and monitor community participation so
> that privileges can be extended to those that contribute.
>
> Community
>
> Acceptance into the Apache foundation would bolster the already strong
> user and developer community around PredictionIO. That community includes
> many contributors from various other companies, and an active mailing list
> composed of hundreds of users.
>
> Core Developers
>
> The core developers of our project are listed in our contributors and
> initial PPMC below. Though many are employed at Salesforce.com, there are
> also engineers from ActionML, and independent developers.
>
> Alignment
>
> The ASF is the natural choice to host the PredictionIO project as its goal
> is democratizing Machine Learning by making it more easily accessible to
> every user/developer. PredictionIO is built on top of several top level
> Apache projects as outlined above.
>
> Known Risks
>
> Orphaned Products
>
> PredictionIO has a solid and growing community. It is deployed on
> production environments by companies of all sizes to run various kinds of
> predictive engines.
>
> In addition to the community contribution to PredictionIO framework, the
> community is also actively contributing new engines to the Template
> Gallery as well as SDKs and documentation for the project. Salesforce is
> committed to utilize and advance the PredictionIO code base and support
> its user community.
>
> Inexperience with Open Source
>
> PredictionIO has existed as a healthy open source project for almost two
> years and is the most starred Scala project on GitHub. All of the proposed
> committers have contributed to ASF and Linux Foundation open source
> projects. Several current committers on Apache projects and Apache Members
> are involved in this proposal and intend to provide mentorship.
>
> Homogeneous Developers
>
> The initial list of committers includes developers from several
> institutions, including Salesforce, ActionML, Channel4, USC as well as
> unaffiliated developers.
>
> Reliance on Salaried Developers
>
> Like most open source projects, PredictionIO receives substantial support
> from salaried developers. PredictionIO development is partially supported
> by Salesforce.com, but there are many contributors from various other
> companies, and an active mailing list composed of hundreds of users. We
> will continue our efforts to ensure stewardship of the project to be
> independent of salaried developers by meritocratically promoting those
> contributors to committers.
>
> Relationships with Other Apache Product
>
> PredictionIO relies heavily on top level Apache projects such as Apache
> Spark, HBase and Hadoop. However it brings a distinguished functionality,
> rather than just an abstraction - Machine Learning in a plug-and-play
> fashion.
>
> Compared to Apache Mahout, which focuses on the development of a wide
> variety of algorithms, PredictionIO offers a platform to manage the whole
> machine learning workflow, including data collection, data preparation,
> modeling, deployment and management of predictive services in production
> environments.
>
> An Excessive Fascination with the Apache Brand
>
> PredictionIO is already a widely known open source project. This proposal
> is not for the purpose of generating publicity. Rather, the primary
> benefits to joining Apache are those outlined in the Rationale section.
>
> Documentation
>
> PredictionIO boasts rich and live documentation, included in the code repo
> (docs/manual directory), is built with Middleman, and publicly hosted at
> https://docs.prediction.io
>
> Initial Source and Intellectual Property Submission Plan
>
> Currently, the PredictionIO codebase is distributed under the Apache 2.0
> License and hosted on GitHub: https://github.com/PredictionIO/PredictionIO
>
> External Dependencies
>
> PredictionIO has the following external dependencies:
> * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are
> needed)
> * Apache Spark 1.3.0 for Hadoop 2.4
> * Java SE Development Kit 8
> * and one of the following sets:
> * PostgreSQL 9.1
> or
> * MySQL 5.1
> or
> * Apache HBase 0.98.6
> * Elasticsearch 1.4.0
>
> Upon acceptance to the incubator, we would begin a thorough analysis of
> all transitive dependencies to verify this information and introduce
> license checking into the build and release process by integrating with
> Apache RAT.
>
> Cryptography
>
> PredictionIO does not include cryptographic code. We utilize standard
> JCE and JSSE APIs provided by the Java Runtime Environment.
>
> Required Resources
>
> We request that following resources be created for the project to use
>
> Mailing lists
>
> predictionio-private@incubator.apache.org (with moderated subscriptions)
> predictionio-dev
> predictionio-user
> predictionio-commits
>
> We will migrate the existing PredictionIO mailing lists.
>
> Git repository
>
> The PredictionIO team would like to use Git for source control, due to
> our
> current use of GitHub.
>
> git://git.apache.org/incubator-predictionio
>
> Documentation
>
> https://predictionio.incubator.apache.org/docs/
>
> JIRA instance
>
> PredictionIO currently uses the GitHub issue tracking system associated
> with its repository: https://github.com/PredictionIO/PredictionIO/issues
> .
> We will migrate to Apache JIRA.
>
> JIRA PREDICTIONIO
> https://issues.apache.org/jira/browse/PREDICTIONIO
>
> Other Resources
>
> TravisCI for builds and test running.
>
> PredictionIO's documentation, included in the code repo (docs/manual
> directory), is built with Middleman and publicly hosted at
> https://docs.prediction.io
>
> A blog to drive adoption and excitement at https://blog.prediction.io
>
> Initial Committers
>
> Pat Ferrell
> Tamas Jambor
> Justin Yip
> Xusen Yin
> Lee Moon Soo
> Donald Szeto
> Kenneth Chan
> Tom Chan
> Simon Chan
> Marco Vivero
> Matthew Tovbin
> Yevgeny Khodorkovsky
> Felipe Oliveira
> Vitaly Gordon
> Alex Merritt
>
> Affiliations
>
> Pat Ferrell - ActionML
> Tamas Jambor - Channel4
> Justin Yip - independent
> Xusen Yin - USC
> Lee Moon Soo - NFLabs
> Donald Szeto - Salesforce
> Kenneth Chan - Salesforce
> Tom Chan - Salesforce
> Simon Chan - Salesforce
> Marco Vivero - Salesforce
> Matthew Tovbin - Salesforce
> Yevgeny Khodorkovsky - Salesforce
> Felipe Oliveira - Salesforce
> Vitaly Gordon - Salesforce
> Alex Merritt - ActionML
>
> Sponsors
>
> Champion
>
> Andrew Purtell <apurtell at apache dot org>
>
> Nominated Mentors
>
> Andrew Purtell <apurtell at apache dot org>
> James Taylor <jtaylor at apache dot org>
> Lars Hofhansl <larsh at apache dot org>
> Suneel Marthi <smarthi at apache dot org>
> Xiangrui Meng <meng at apache dot org>
> Luciano Resende <lresende at apache dot org>
>
> Sponsoring Entity
>
> Apache Incubator PMC
>
>
> --
> Best regards,
>
> - Andy
>
> Problems worthy of attack prove their worth by hitting back. - Piet Hein
> (via Tom White)
>
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Ashish <pa...@gmail.com>.
+1 (non-binding)
On Mon, May 23, 2016 at 3:22 PM, Andrew Purtell <ap...@apache.org> wrote:
> Since discussion on the matter of PredictionIO has died down, I would like
> to call a VOTE
> on accepting PredictionIO into the Apache Incubator.
>
> Proposal: https://wiki.apache.org/incubator/PredictionIO
>
> [ ] +1 Accept PredictionIO into the Apache Incubator
> [ ] +0 Abstain
> [ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
>
> This vote will be open for at least 72 hours.
>
> My vote is +1 (binding)
>
> --
>
> PredictionIO Proposal
>
> Abstract
>
> PredictionIO is an open source Machine Learning Server built on top of
> state-of-the-art open source stack, that enables developers to manage and
> deploy production-ready predictive services for various kinds of machine
> learning tasks.
>
> Proposal
>
> The PredictionIO platform consists of the following components:
>
> * PredictionIO framework - provides the machine learning stack for
> building, evaluating and deploying engines with machine learning
> algorithms. It uses Apache Spark for processing.
>
> * Event Server - the machine learning analytics layer for unifying events
> from multiple platforms. It can use Apache HBase or any JDBC backends
> as its data store.
>
> The PredictionIO community also maintains a Template Gallery, a place to
> publish and download (free or proprietary) engine templates for different
> types of machine learning applications, and is a complemental part of the
> project. At this point we exclude the Template Gallery from the proposal,
> as it has a separate set of contributors and we’re not familiar with an
> Apache approved mechanism to maintain such a gallery.
>
> Background
>
> PredictionIO was started with a mission to democratize and bring machine
> learning to the masses.
>
> Machine learning has traditionally been a luxury for big companies like
> Google, Facebook, and Netflix. There are ML libraries and tools lying
> around the internet but the effort of putting them all together as a
> production-ready infrastructure is a very resource-intensive task that is
> remotely reachable by individuals or small businesses.
>
> PredictionIO is a production-ready, full stack machine learning system that
> allows organizations of any scale to quickly deploy machine learning
> capabilities. It comes with official and community-contributed machine
> learning engine templates that are easy to customize.
>
> Rationale
>
> As usage and number of contributors to PredictionIO has grown bigger and
> more diverse, we have sought for an independent framework for the project
> to keep thriving. We believe the Apache foundation is a great fit. Joining
> Apache would ensure that tried and true processes and procedures are in
> place for the growing number of organizations interested in contributing
> to PredictionIO. PredictionIO is also a good fit for the Apache foundation.
> PredictionIO was built on top of several Apache projects (HBase, Spark,
> Hadoop). We are familiar with the Apache process and believe that the
> democratic and meritocratic nature of the foundation aligns with the
> project goals.
>
> Initial Goals
>
> The initial milestones will be to move the existing codebase to Apache and
> integrate with the Apache development process. Once this is accomplished,
> we plan for incremental development and releases that follow the Apache
> guidelines, as well as growing our developer and user communities.
>
> Current Status
>
> PredictionIO has undergone nine minor releases and many patches.
> PredictionIO is being used in production by Salesforce.com as well as many
> other organizations and apps. The PredictionIO codebase is currently
> hosted at GitHub, which will form the basis of the Apache git repository.
>
> Meritocracy
>
> We plan to invest in supporting a meritocracy. We will discuss the
> requirements in an open forum. We intend to invite additional developers
> to participate. We will encourage and monitor community participation so
> that privileges can be extended to those that contribute.
>
> Community
>
> Acceptance into the Apache foundation would bolster the already strong
> user and developer community around PredictionIO. That community includes
> many contributors from various other companies, and an active mailing list
> composed of hundreds of users.
>
> Core Developers
>
> The core developers of our project are listed in our contributors and
> initial PPMC below. Though many are employed at Salesforce.com, there are
> also engineers from ActionML, and independent developers.
>
> Alignment
>
> The ASF is the natural choice to host the PredictionIO project as its goal
> is democratizing Machine Learning by making it more easily accessible to
> every user/developer. PredictionIO is built on top of several top level
> Apache projects as outlined above.
>
> Known Risks
>
> Orphaned Products
>
> PredictionIO has a solid and growing community. It is deployed on
> production environments by companies of all sizes to run various kinds of
> predictive engines.
>
> In addition to the community contribution to PredictionIO framework, the
> community is also actively contributing new engines to the Template
> Gallery as well as SDKs and documentation for the project. Salesforce is
> committed to utilize and advance the PredictionIO code base and support
> its user community.
>
> Inexperience with Open Source
>
> PredictionIO has existed as a healthy open source project for almost two
> years and is the most starred Scala project on GitHub. All of the proposed
> committers have contributed to ASF and Linux Foundation open source
> projects. Several current committers on Apache projects and Apache Members
> are involved in this proposal and intend to provide mentorship.
>
> Homogeneous Developers
>
> The initial list of committers includes developers from several
> institutions, including Salesforce, ActionML, Channel4, USC as well as
> unaffiliated developers.
>
> Reliance on Salaried Developers
>
> Like most open source projects, PredictionIO receives substantial support
> from salaried developers. PredictionIO development is partially supported
> by Salesforce.com, but there are many contributors from various other
> companies, and an active mailing list composed of hundreds of users. We
> will continue our efforts to ensure stewardship of the project to be
> independent of salaried developers by meritocratically promoting those
> contributors to committers.
>
> Relationships with Other Apache Product
>
> PredictionIO relies heavily on top level Apache projects such as Apache
> Spark, HBase and Hadoop. However it brings a distinguished functionality,
> rather than just an abstraction - Machine Learning in a plug-and-play
> fashion.
>
> Compared to Apache Mahout, which focuses on the development of a wide
> variety of algorithms, PredictionIO offers a platform to manage the whole
> machine learning workflow, including data collection, data preparation,
> modeling, deployment and management of predictive services in production
> environments.
>
> An Excessive Fascination with the Apache Brand
>
> PredictionIO is already a widely known open source project. This proposal
> is not for the purpose of generating publicity. Rather, the primary
> benefits to joining Apache are those outlined in the Rationale section.
>
> Documentation
>
> PredictionIO boasts rich and live documentation, included in the code repo
> (docs/manual directory), is built with Middleman, and publicly hosted at
> https://docs.prediction.io
>
> Initial Source and Intellectual Property Submission Plan
>
> Currently, the PredictionIO codebase is distributed under the Apache 2.0
> License and hosted on GitHub: https://github.com/PredictionIO/PredictionIO
>
> External Dependencies
>
> PredictionIO has the following external dependencies:
> * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are needed)
> * Apache Spark 1.3.0 for Hadoop 2.4
> * Java SE Development Kit 8
> * and one of the following sets:
> * PostgreSQL 9.1
> or
> * MySQL 5.1
> or
> * Apache HBase 0.98.6
> * Elasticsearch 1.4.0
>
> Upon acceptance to the incubator, we would begin a thorough analysis of
> all transitive dependencies to verify this information and introduce
> license checking into the build and release process by integrating with
> Apache RAT.
>
> Cryptography
>
> PredictionIO does not include cryptographic code. We utilize standard
> JCE and JSSE APIs provided by the Java Runtime Environment.
>
> Required Resources
>
> We request that following resources be created for the project to use
>
> Mailing lists
>
> predictionio-private@incubator.apache.org (with moderated subscriptions)
> predictionio-dev
> predictionio-user
> predictionio-commits
>
> We will migrate the existing PredictionIO mailing lists.
>
> Git repository
>
> The PredictionIO team would like to use Git for source control, due to our
> current use of GitHub.
>
> git://git.apache.org/incubator-predictionio
>
> Documentation
>
> https://predictionio.incubator.apache.org/docs/
>
> JIRA instance
>
> PredictionIO currently uses the GitHub issue tracking system associated
> with its repository: https://github.com/PredictionIO/PredictionIO/issues.
> We will migrate to Apache JIRA.
>
> JIRA PREDICTIONIO
> https://issues.apache.org/jira/browse/PREDICTIONIO
>
> Other Resources
>
> TravisCI for builds and test running.
>
> PredictionIO's documentation, included in the code repo (docs/manual
> directory), is built with Middleman and publicly hosted at
> https://docs.prediction.io
>
> A blog to drive adoption and excitement at https://blog.prediction.io
>
> Initial Committers
>
> Pat Ferrell
> Tamas Jambor
> Justin Yip
> Xusen Yin
> Lee Moon Soo
> Donald Szeto
> Kenneth Chan
> Tom Chan
> Simon Chan
> Marco Vivero
> Matthew Tovbin
> Yevgeny Khodorkovsky
> Felipe Oliveira
> Vitaly Gordon
> Alex Merritt
>
> Affiliations
>
> Pat Ferrell - ActionML
> Tamas Jambor - Channel4
> Justin Yip - independent
> Xusen Yin - USC
> Lee Moon Soo - NFLabs
> Donald Szeto - Salesforce
> Kenneth Chan - Salesforce
> Tom Chan - Salesforce
> Simon Chan - Salesforce
> Marco Vivero - Salesforce
> Matthew Tovbin - Salesforce
> Yevgeny Khodorkovsky - Salesforce
> Felipe Oliveira - Salesforce
> Vitaly Gordon - Salesforce
> Alex Merritt - ActionML
>
> Sponsors
>
> Champion
>
> Andrew Purtell <apurtell at apache dot org>
>
> Nominated Mentors
>
> Andrew Purtell <apurtell at apache dot org>
> James Taylor <jtaylor at apache dot org>
> Lars Hofhansl <larsh at apache dot org>
> Suneel Marthi <smarthi at apache dot org>
> Xiangrui Meng <meng at apache dot org>
> Luciano Resende <lresende at apache dot org>
>
> Sponsoring Entity
>
> Apache Incubator PMC
>
>
> --
> Best regards,
>
> - Andy
>
> Problems worthy of attack prove their worth by hitting back. - Piet Hein
> (via Tom White)
--
thanks
ashish
Blog: http://www.ashishpaliwal.com/blog
My Photo Galleries: http://www.pbase.com/ashishpaliwal
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Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Jake Farrell <jf...@apache.org>.
+1
-Jake
On Mon, May 23, 2016 at 6:22 PM, Andrew Purtell <ap...@apache.org> wrote:
> Since discussion on the matter of PredictionIO has died down, I would like
> to call a VOTE
> on accepting PredictionIO into the Apache Incubator.
>
> Proposal: https://wiki.apache.org/incubator/PredictionIO
>
> [ ] +1 Accept PredictionIO into the Apache Incubator
> [ ] +0 Abstain
> [ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
>
> This vote will be open for at least 72 hours.
>
> My vote is +1 (binding)
>
> --
>
> PredictionIO Proposal
>
> Abstract
>
> PredictionIO is an open source Machine Learning Server built on top of
> state-of-the-art open source stack, that enables developers to manage and
> deploy production-ready predictive services for various kinds of machine
> learning tasks.
>
> Proposal
>
> The PredictionIO platform consists of the following components:
>
> * PredictionIO framework - provides the machine learning stack for
> building, evaluating and deploying engines with machine learning
> algorithms. It uses Apache Spark for processing.
>
> * Event Server - the machine learning analytics layer for unifying
> events
> from multiple platforms. It can use Apache HBase or any JDBC backends
> as its data store.
>
> The PredictionIO community also maintains a Template Gallery, a place to
> publish and download (free or proprietary) engine templates for different
> types of machine learning applications, and is a complemental part of the
> project. At this point we exclude the Template Gallery from the proposal,
> as it has a separate set of contributors and we’re not familiar with an
> Apache approved mechanism to maintain such a gallery.
>
> Background
>
> PredictionIO was started with a mission to democratize and bring machine
> learning to the masses.
>
> Machine learning has traditionally been a luxury for big companies like
> Google, Facebook, and Netflix. There are ML libraries and tools lying
> around the internet but the effort of putting them all together as a
> production-ready infrastructure is a very resource-intensive task that is
> remotely reachable by individuals or small businesses.
>
> PredictionIO is a production-ready, full stack machine learning system that
> allows organizations of any scale to quickly deploy machine learning
> capabilities. It comes with official and community-contributed machine
> learning engine templates that are easy to customize.
>
> Rationale
>
> As usage and number of contributors to PredictionIO has grown bigger and
> more diverse, we have sought for an independent framework for the project
> to keep thriving. We believe the Apache foundation is a great fit. Joining
> Apache would ensure that tried and true processes and procedures are in
> place for the growing number of organizations interested in contributing
> to PredictionIO. PredictionIO is also a good fit for the Apache foundation.
> PredictionIO was built on top of several Apache projects (HBase, Spark,
> Hadoop). We are familiar with the Apache process and believe that the
> democratic and meritocratic nature of the foundation aligns with the
> project goals.
>
> Initial Goals
>
> The initial milestones will be to move the existing codebase to Apache and
> integrate with the Apache development process. Once this is accomplished,
> we plan for incremental development and releases that follow the Apache
> guidelines, as well as growing our developer and user communities.
>
> Current Status
>
> PredictionIO has undergone nine minor releases and many patches.
> PredictionIO is being used in production by Salesforce.com as well as many
> other organizations and apps. The PredictionIO codebase is currently
> hosted at GitHub, which will form the basis of the Apache git repository.
>
> Meritocracy
>
> We plan to invest in supporting a meritocracy. We will discuss the
> requirements in an open forum. We intend to invite additional developers
> to participate. We will encourage and monitor community participation so
> that privileges can be extended to those that contribute.
>
> Community
>
> Acceptance into the Apache foundation would bolster the already strong
> user and developer community around PredictionIO. That community includes
> many contributors from various other companies, and an active mailing list
> composed of hundreds of users.
>
> Core Developers
>
> The core developers of our project are listed in our contributors and
> initial PPMC below. Though many are employed at Salesforce.com, there are
> also engineers from ActionML, and independent developers.
>
> Alignment
>
> The ASF is the natural choice to host the PredictionIO project as its goal
> is democratizing Machine Learning by making it more easily accessible to
> every user/developer. PredictionIO is built on top of several top level
> Apache projects as outlined above.
>
> Known Risks
>
> Orphaned Products
>
> PredictionIO has a solid and growing community. It is deployed on
> production environments by companies of all sizes to run various kinds of
> predictive engines.
>
> In addition to the community contribution to PredictionIO framework, the
> community is also actively contributing new engines to the Template
> Gallery as well as SDKs and documentation for the project. Salesforce is
> committed to utilize and advance the PredictionIO code base and support
> its user community.
>
> Inexperience with Open Source
>
> PredictionIO has existed as a healthy open source project for almost two
> years and is the most starred Scala project on GitHub. All of the proposed
> committers have contributed to ASF and Linux Foundation open source
> projects. Several current committers on Apache projects and Apache Members
> are involved in this proposal and intend to provide mentorship.
>
> Homogeneous Developers
>
> The initial list of committers includes developers from several
> institutions, including Salesforce, ActionML, Channel4, USC as well as
> unaffiliated developers.
>
> Reliance on Salaried Developers
>
> Like most open source projects, PredictionIO receives substantial support
> from salaried developers. PredictionIO development is partially supported
> by Salesforce.com, but there are many contributors from various other
> companies, and an active mailing list composed of hundreds of users. We
> will continue our efforts to ensure stewardship of the project to be
> independent of salaried developers by meritocratically promoting those
> contributors to committers.
>
> Relationships with Other Apache Product
>
> PredictionIO relies heavily on top level Apache projects such as Apache
> Spark, HBase and Hadoop. However it brings a distinguished functionality,
> rather than just an abstraction - Machine Learning in a plug-and-play
> fashion.
>
> Compared to Apache Mahout, which focuses on the development of a wide
> variety of algorithms, PredictionIO offers a platform to manage the whole
> machine learning workflow, including data collection, data preparation,
> modeling, deployment and management of predictive services in production
> environments.
>
> An Excessive Fascination with the Apache Brand
>
> PredictionIO is already a widely known open source project. This proposal
> is not for the purpose of generating publicity. Rather, the primary
> benefits to joining Apache are those outlined in the Rationale section.
>
> Documentation
>
> PredictionIO boasts rich and live documentation, included in the code repo
> (docs/manual directory), is built with Middleman, and publicly hosted at
> https://docs.prediction.io
>
> Initial Source and Intellectual Property Submission Plan
>
> Currently, the PredictionIO codebase is distributed under the Apache 2.0
> License and hosted on GitHub: https://github.com/PredictionIO/PredictionIO
>
> External Dependencies
>
> PredictionIO has the following external dependencies:
> * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are
> needed)
> * Apache Spark 1.3.0 for Hadoop 2.4
> * Java SE Development Kit 8
> * and one of the following sets:
> * PostgreSQL 9.1
> or
> * MySQL 5.1
> or
> * Apache HBase 0.98.6
> * Elasticsearch 1.4.0
>
> Upon acceptance to the incubator, we would begin a thorough analysis of
> all transitive dependencies to verify this information and introduce
> license checking into the build and release process by integrating with
> Apache RAT.
>
> Cryptography
>
> PredictionIO does not include cryptographic code. We utilize standard
> JCE and JSSE APIs provided by the Java Runtime Environment.
>
> Required Resources
>
> We request that following resources be created for the project to use
>
> Mailing lists
>
> predictionio-private@incubator.apache.org (with moderated subscriptions)
> predictionio-dev
> predictionio-user
> predictionio-commits
>
> We will migrate the existing PredictionIO mailing lists.
>
> Git repository
>
> The PredictionIO team would like to use Git for source control, due to
> our
> current use of GitHub.
>
> git://git.apache.org/incubator-predictionio
>
> Documentation
>
> https://predictionio.incubator.apache.org/docs/
>
> JIRA instance
>
> PredictionIO currently uses the GitHub issue tracking system associated
> with its repository: https://github.com/PredictionIO/PredictionIO/issues
> .
> We will migrate to Apache JIRA.
>
> JIRA PREDICTIONIO
> https://issues.apache.org/jira/browse/PREDICTIONIO
>
> Other Resources
>
> TravisCI for builds and test running.
>
> PredictionIO's documentation, included in the code repo (docs/manual
> directory), is built with Middleman and publicly hosted at
> https://docs.prediction.io
>
> A blog to drive adoption and excitement at https://blog.prediction.io
>
> Initial Committers
>
> Pat Ferrell
> Tamas Jambor
> Justin Yip
> Xusen Yin
> Lee Moon Soo
> Donald Szeto
> Kenneth Chan
> Tom Chan
> Simon Chan
> Marco Vivero
> Matthew Tovbin
> Yevgeny Khodorkovsky
> Felipe Oliveira
> Vitaly Gordon
> Alex Merritt
>
> Affiliations
>
> Pat Ferrell - ActionML
> Tamas Jambor - Channel4
> Justin Yip - independent
> Xusen Yin - USC
> Lee Moon Soo - NFLabs
> Donald Szeto - Salesforce
> Kenneth Chan - Salesforce
> Tom Chan - Salesforce
> Simon Chan - Salesforce
> Marco Vivero - Salesforce
> Matthew Tovbin - Salesforce
> Yevgeny Khodorkovsky - Salesforce
> Felipe Oliveira - Salesforce
> Vitaly Gordon - Salesforce
> Alex Merritt - ActionML
>
> Sponsors
>
> Champion
>
> Andrew Purtell <apurtell at apache dot org>
>
> Nominated Mentors
>
> Andrew Purtell <apurtell at apache dot org>
> James Taylor <jtaylor at apache dot org>
> Lars Hofhansl <larsh at apache dot org>
> Suneel Marthi <smarthi at apache dot org>
> Xiangrui Meng <meng at apache dot org>
> Luciano Resende <lresende at apache dot org>
>
> Sponsoring Entity
>
> Apache Incubator PMC
>
>
> --
> Best regards,
>
> - Andy
>
> Problems worthy of attack prove their worth by hitting back. - Piet Hein
> (via Tom White)
>
RE: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Priyank Ashok Rastogi <pr...@huawei.com>.
+1 Accept PredictionIO into the Apache Incubator
-----Original Message-----
From: Andrew Purtell [mailto:apurtell@apache.org]
Sent: 24 May 2016 03:52
To: general@incubator.apache.org
Subject: [VOTE] Accept PredictionIO into the Apache Incubator
Since discussion on the matter of PredictionIO has died down, I would like to call a VOTE on accepting PredictionIO into the Apache Incubator.
Proposal: https://wiki.apache.org/incubator/PredictionIO
[ ] +1 Accept PredictionIO into the Apache Incubator [ ] +0 Abstain [ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
This vote will be open for at least 72 hours.
My vote is +1 (binding)
--
PredictionIO Proposal
Abstract
PredictionIO is an open source Machine Learning Server built on top of state-of-the-art open source stack, that enables developers to manage and deploy production-ready predictive services for various kinds of machine learning tasks.
Proposal
The PredictionIO platform consists of the following components:
* PredictionIO framework - provides the machine learning stack for
building, evaluating and deploying engines with machine learning
algorithms. It uses Apache Spark for processing.
* Event Server - the machine learning analytics layer for unifying events
from multiple platforms. It can use Apache HBase or any JDBC backends
as its data store.
The PredictionIO community also maintains a Template Gallery, a place to publish and download (free or proprietary) engine templates for different types of machine learning applications, and is a complemental part of the project. At this point we exclude the Template Gallery from the proposal, as it has a separate set of contributors and we’re not familiar with an Apache approved mechanism to maintain such a gallery.
Background
PredictionIO was started with a mission to democratize and bring machine learning to the masses.
Machine learning has traditionally been a luxury for big companies like Google, Facebook, and Netflix. There are ML libraries and tools lying around the internet but the effort of putting them all together as a production-ready infrastructure is a very resource-intensive task that is remotely reachable by individuals or small businesses.
PredictionIO is a production-ready, full stack machine learning system that allows organizations of any scale to quickly deploy machine learning capabilities. It comes with official and community-contributed machine learning engine templates that are easy to customize.
Rationale
As usage and number of contributors to PredictionIO has grown bigger and more diverse, we have sought for an independent framework for the project to keep thriving. We believe the Apache foundation is a great fit. Joining Apache would ensure that tried and true processes and procedures are in place for the growing number of organizations interested in contributing to PredictionIO. PredictionIO is also a good fit for the Apache foundation.
PredictionIO was built on top of several Apache projects (HBase, Spark, Hadoop). We are familiar with the Apache process and believe that the democratic and meritocratic nature of the foundation aligns with the project goals.
Initial Goals
The initial milestones will be to move the existing codebase to Apache and integrate with the Apache development process. Once this is accomplished, we plan for incremental development and releases that follow the Apache guidelines, as well as growing our developer and user communities.
Current Status
PredictionIO has undergone nine minor releases and many patches.
PredictionIO is being used in production by Salesforce.com as well as many other organizations and apps. The PredictionIO codebase is currently hosted at GitHub, which will form the basis of the Apache git repository.
Meritocracy
We plan to invest in supporting a meritocracy. We will discuss the requirements in an open forum. We intend to invite additional developers to participate. We will encourage and monitor community participation so that privileges can be extended to those that contribute.
Community
Acceptance into the Apache foundation would bolster the already strong user and developer community around PredictionIO. That community includes many contributors from various other companies, and an active mailing list composed of hundreds of users.
Core Developers
The core developers of our project are listed in our contributors and initial PPMC below. Though many are employed at Salesforce.com, there are also engineers from ActionML, and independent developers.
Alignment
The ASF is the natural choice to host the PredictionIO project as its goal is democratizing Machine Learning by making it more easily accessible to every user/developer. PredictionIO is built on top of several top level Apache projects as outlined above.
Known Risks
Orphaned Products
PredictionIO has a solid and growing community. It is deployed on production environments by companies of all sizes to run various kinds of predictive engines.
In addition to the community contribution to PredictionIO framework, the community is also actively contributing new engines to the Template Gallery as well as SDKs and documentation for the project. Salesforce is committed to utilize and advance the PredictionIO code base and support its user community.
Inexperience with Open Source
PredictionIO has existed as a healthy open source project for almost two years and is the most starred Scala project on GitHub. All of the proposed committers have contributed to ASF and Linux Foundation open source projects. Several current committers on Apache projects and Apache Members are involved in this proposal and intend to provide mentorship.
Homogeneous Developers
The initial list of committers includes developers from several institutions, including Salesforce, ActionML, Channel4, USC as well as unaffiliated developers.
Reliance on Salaried Developers
Like most open source projects, PredictionIO receives substantial support from salaried developers. PredictionIO development is partially supported by Salesforce.com, but there are many contributors from various other companies, and an active mailing list composed of hundreds of users. We will continue our efforts to ensure stewardship of the project to be independent of salaried developers by meritocratically promoting those contributors to committers.
Relationships with Other Apache Product
PredictionIO relies heavily on top level Apache projects such as Apache Spark, HBase and Hadoop. However it brings a distinguished functionality, rather than just an abstraction - Machine Learning in a plug-and-play fashion.
Compared to Apache Mahout, which focuses on the development of a wide variety of algorithms, PredictionIO offers a platform to manage the whole machine learning workflow, including data collection, data preparation, modeling, deployment and management of predictive services in production environments.
An Excessive Fascination with the Apache Brand
PredictionIO is already a widely known open source project. This proposal is not for the purpose of generating publicity. Rather, the primary benefits to joining Apache are those outlined in the Rationale section.
Documentation
PredictionIO boasts rich and live documentation, included in the code repo (docs/manual directory), is built with Middleman, and publicly hosted at https://docs.prediction.io
Initial Source and Intellectual Property Submission Plan
Currently, the PredictionIO codebase is distributed under the Apache 2.0 License and hosted on GitHub: https://github.com/PredictionIO/PredictionIO
External Dependencies
PredictionIO has the following external dependencies:
* Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are needed)
* Apache Spark 1.3.0 for Hadoop 2.4
* Java SE Development Kit 8
* and one of the following sets:
* PostgreSQL 9.1
or
* MySQL 5.1
or
* Apache HBase 0.98.6
* Elasticsearch 1.4.0
Upon acceptance to the incubator, we would begin a thorough analysis of all transitive dependencies to verify this information and introduce license checking into the build and release process by integrating with Apache RAT.
Cryptography
PredictionIO does not include cryptographic code. We utilize standard JCE and JSSE APIs provided by the Java Runtime Environment.
Required Resources
We request that following resources be created for the project to use
Mailing lists
predictionio-private@incubator.apache.org (with moderated subscriptions)
predictionio-dev
predictionio-user
predictionio-commits
We will migrate the existing PredictionIO mailing lists.
Git repository
The PredictionIO team would like to use Git for source control, due to our
current use of GitHub.
git://git.apache.org/incubator-predictionio
Documentation
https://predictionio.incubator.apache.org/docs/
JIRA instance
PredictionIO currently uses the GitHub issue tracking system associated
with its repository: https://github.com/PredictionIO/PredictionIO/issues.
We will migrate to Apache JIRA.
JIRA PREDICTIONIO
https://issues.apache.org/jira/browse/PREDICTIONIO
Other Resources
TravisCI for builds and test running.
PredictionIO's documentation, included in the code repo (docs/manual
directory), is built with Middleman and publicly hosted at
https://docs.prediction.io
A blog to drive adoption and excitement at https://blog.prediction.io
Initial Committers
Pat Ferrell
Tamas Jambor
Justin Yip
Xusen Yin
Lee Moon Soo
Donald Szeto
Kenneth Chan
Tom Chan
Simon Chan
Marco Vivero
Matthew Tovbin
Yevgeny Khodorkovsky
Felipe Oliveira
Vitaly Gordon
Alex Merritt
Affiliations
Pat Ferrell - ActionML
Tamas Jambor - Channel4
Justin Yip - independent
Xusen Yin - USC
Lee Moon Soo - NFLabs
Donald Szeto - Salesforce
Kenneth Chan - Salesforce
Tom Chan - Salesforce
Simon Chan - Salesforce
Marco Vivero - Salesforce
Matthew Tovbin - Salesforce
Yevgeny Khodorkovsky - Salesforce
Felipe Oliveira - Salesforce
Vitaly Gordon - Salesforce
Alex Merritt - ActionML
Sponsors
Champion
Andrew Purtell <apurtell at apache dot org>
Nominated Mentors
Andrew Purtell <apurtell at apache dot org>
James Taylor <jtaylor at apache dot org>
Lars Hofhansl <larsh at apache dot org>
Suneel Marthi <smarthi at apache dot org>
Xiangrui Meng <meng at apache dot org>
Luciano Resende <lresende at apache dot org>
Sponsoring Entity
Apache Incubator PMC
--
Best regards,
- Andy
Problems worthy of attack prove their worth by hitting back. - Piet Hein (via Tom White)
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Sandeep Deshmukh <sa...@datatorrent.com>.
+ 1 (non-binding)
Regards,
Sandeep
On Wed, May 25, 2016 at 10:38 AM, Paul Fremantle <pz...@gmail.com> wrote:
> +1 (binding)
> Paul
>
> On Wed, May 25, 2016 at 5:12 AM, Tsuyoshi Ozawa <oz...@apache.org> wrote:
>
> > +1 (non-binding)
> > - Tsuyoshi
> >
> > On Wed, May 25, 2016 at 1:00 PM, Reynold Xin <rx...@databricks.com>
> wrote:
> > > +1 (binding)
> > >
> > >
> > > On Mon, May 23, 2016 at 3:22 PM, Andrew Purtell <ap...@apache.org>
> > wrote:
> > >
> > >> Since discussion on the matter of PredictionIO has died down, I would
> > like
> > >> to call a VOTE
> > >> on accepting PredictionIO into the Apache Incubator.
> > >>
> > >> Proposal: https://wiki.apache.org/incubator/PredictionIO
> > >>
> > >> [ ] +1 Accept PredictionIO into the Apache Incubator
> > >> [ ] +0 Abstain
> > >> [ ] -1 Do not accept PredictionIO into the Apache Incubator, because
> ...
> > >>
> > >> This vote will be open for at least 72 hours.
> > >>
> > >> My vote is +1 (binding)
> > >>
> > >> --
> > >>
> > >> PredictionIO Proposal
> > >>
> > >> Abstract
> > >>
> > >> PredictionIO is an open source Machine Learning Server built on top of
> > >> state-of-the-art open source stack, that enables developers to manage
> > and
> > >> deploy production-ready predictive services for various kinds of
> machine
> > >> learning tasks.
> > >>
> > >> Proposal
> > >>
> > >> The PredictionIO platform consists of the following components:
> > >>
> > >> * PredictionIO framework - provides the machine learning stack for
> > >> building, evaluating and deploying engines with machine learning
> > >> algorithms. It uses Apache Spark for processing.
> > >>
> > >> * Event Server - the machine learning analytics layer for unifying
> > >> events
> > >> from multiple platforms. It can use Apache HBase or any JDBC
> > backends
> > >> as its data store.
> > >>
> > >> The PredictionIO community also maintains a Template Gallery, a place
> to
> > >> publish and download (free or proprietary) engine templates for
> > different
> > >> types of machine learning applications, and is a complemental part of
> > the
> > >> project. At this point we exclude the Template Gallery from the
> > proposal,
> > >> as it has a separate set of contributors and we’re not familiar with
> an
> > >> Apache approved mechanism to maintain such a gallery.
> > >>
> > >> Background
> > >>
> > >> PredictionIO was started with a mission to democratize and bring
> machine
> > >> learning to the masses.
> > >>
> > >> Machine learning has traditionally been a luxury for big companies
> like
> > >> Google, Facebook, and Netflix. There are ML libraries and tools lying
> > >> around the internet but the effort of putting them all together as a
> > >> production-ready infrastructure is a very resource-intensive task that
> > is
> > >> remotely reachable by individuals or small businesses.
> > >>
> > >> PredictionIO is a production-ready, full stack machine learning system
> > that
> > >> allows organizations of any scale to quickly deploy machine learning
> > >> capabilities. It comes with official and community-contributed machine
> > >> learning engine templates that are easy to customize.
> > >>
> > >> Rationale
> > >>
> > >> As usage and number of contributors to PredictionIO has grown bigger
> and
> > >> more diverse, we have sought for an independent framework for the
> > project
> > >> to keep thriving. We believe the Apache foundation is a great fit.
> > Joining
> > >> Apache would ensure that tried and true processes and procedures are
> in
> > >> place for the growing number of organizations interested in
> contributing
> > >> to PredictionIO. PredictionIO is also a good fit for the Apache
> > foundation.
> > >> PredictionIO was built on top of several Apache projects (HBase,
> Spark,
> > >> Hadoop). We are familiar with the Apache process and believe that the
> > >> democratic and meritocratic nature of the foundation aligns with the
> > >> project goals.
> > >>
> > >> Initial Goals
> > >>
> > >> The initial milestones will be to move the existing codebase to Apache
> > and
> > >> integrate with the Apache development process. Once this is
> > accomplished,
> > >> we plan for incremental development and releases that follow the
> Apache
> > >> guidelines, as well as growing our developer and user communities.
> > >>
> > >> Current Status
> > >>
> > >> PredictionIO has undergone nine minor releases and many patches.
> > >> PredictionIO is being used in production by Salesforce.com as well as
> > many
> > >> other organizations and apps. The PredictionIO codebase is currently
> > >> hosted at GitHub, which will form the basis of the Apache git
> > repository.
> > >>
> > >> Meritocracy
> > >>
> > >> We plan to invest in supporting a meritocracy. We will discuss the
> > >> requirements in an open forum. We intend to invite additional
> developers
> > >> to participate. We will encourage and monitor community participation
> so
> > >> that privileges can be extended to those that contribute.
> > >>
> > >> Community
> > >>
> > >> Acceptance into the Apache foundation would bolster the already strong
> > >> user and developer community around PredictionIO. That community
> > includes
> > >> many contributors from various other companies, and an active mailing
> > list
> > >> composed of hundreds of users.
> > >>
> > >> Core Developers
> > >>
> > >> The core developers of our project are listed in our contributors and
> > >> initial PPMC below. Though many are employed at Salesforce.com, there
> > are
> > >> also engineers from ActionML, and independent developers.
> > >>
> > >> Alignment
> > >>
> > >> The ASF is the natural choice to host the PredictionIO project as its
> > goal
> > >> is democratizing Machine Learning by making it more easily accessible
> to
> > >> every user/developer. PredictionIO is built on top of several top
> level
> > >> Apache projects as outlined above.
> > >>
> > >> Known Risks
> > >>
> > >> Orphaned Products
> > >>
> > >> PredictionIO has a solid and growing community. It is deployed on
> > >> production environments by companies of all sizes to run various kinds
> > of
> > >> predictive engines.
> > >>
> > >> In addition to the community contribution to PredictionIO framework,
> the
> > >> community is also actively contributing new engines to the Template
> > >> Gallery as well as SDKs and documentation for the project. Salesforce
> is
> > >> committed to utilize and advance the PredictionIO code base and
> support
> > >> its user community.
> > >>
> > >> Inexperience with Open Source
> > >>
> > >> PredictionIO has existed as a healthy open source project for almost
> two
> > >> years and is the most starred Scala project on GitHub. All of the
> > proposed
> > >> committers have contributed to ASF and Linux Foundation open source
> > >> projects. Several current committers on Apache projects and Apache
> > Members
> > >> are involved in this proposal and intend to provide mentorship.
> > >>
> > >> Homogeneous Developers
> > >>
> > >> The initial list of committers includes developers from several
> > >> institutions, including Salesforce, ActionML, Channel4, USC as well as
> > >> unaffiliated developers.
> > >>
> > >> Reliance on Salaried Developers
> > >>
> > >> Like most open source projects, PredictionIO receives substantial
> > support
> > >> from salaried developers. PredictionIO development is partially
> > supported
> > >> by Salesforce.com, but there are many contributors from various other
> > >> companies, and an active mailing list composed of hundreds of users.
> We
> > >> will continue our efforts to ensure stewardship of the project to be
> > >> independent of salaried developers by meritocratically promoting those
> > >> contributors to committers.
> > >>
> > >> Relationships with Other Apache Product
> > >>
> > >> PredictionIO relies heavily on top level Apache projects such as
> Apache
> > >> Spark, HBase and Hadoop. However it brings a distinguished
> > functionality,
> > >> rather than just an abstraction - Machine Learning in a plug-and-play
> > >> fashion.
> > >>
> > >> Compared to Apache Mahout, which focuses on the development of a wide
> > >> variety of algorithms, PredictionIO offers a platform to manage the
> > whole
> > >> machine learning workflow, including data collection, data
> preparation,
> > >> modeling, deployment and management of predictive services in
> production
> > >> environments.
> > >>
> > >> An Excessive Fascination with the Apache Brand
> > >>
> > >> PredictionIO is already a widely known open source project. This
> > proposal
> > >> is not for the purpose of generating publicity. Rather, the primary
> > >> benefits to joining Apache are those outlined in the Rationale
> section.
> > >>
> > >> Documentation
> > >>
> > >> PredictionIO boasts rich and live documentation, included in the code
> > repo
> > >> (docs/manual directory), is built with Middleman, and publicly hosted
> at
> > >> https://docs.prediction.io
> > >>
> > >> Initial Source and Intellectual Property Submission Plan
> > >>
> > >> Currently, the PredictionIO codebase is distributed under the Apache
> 2.0
> > >> License and hosted on GitHub:
> > https://github.com/PredictionIO/PredictionIO
> > >>
> > >> External Dependencies
> > >>
> > >> PredictionIO has the following external dependencies:
> > >> * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are
> > >> needed)
> > >> * Apache Spark 1.3.0 for Hadoop 2.4
> > >> * Java SE Development Kit 8
> > >> * and one of the following sets:
> > >> * PostgreSQL 9.1
> > >> or
> > >> * MySQL 5.1
> > >> or
> > >> * Apache HBase 0.98.6
> > >> * Elasticsearch 1.4.0
> > >>
> > >> Upon acceptance to the incubator, we would begin a thorough analysis
> of
> > >> all transitive dependencies to verify this information and introduce
> > >> license checking into the build and release process by integrating
> with
> > >> Apache RAT.
> > >>
> > >> Cryptography
> > >>
> > >> PredictionIO does not include cryptographic code. We utilize standard
> > >> JCE and JSSE APIs provided by the Java Runtime Environment.
> > >>
> > >> Required Resources
> > >>
> > >> We request that following resources be created for the project to use
> > >>
> > >> Mailing lists
> > >>
> > >> predictionio-private@incubator.apache.org (with moderated
> > subscriptions)
> > >> predictionio-dev
> > >> predictionio-user
> > >> predictionio-commits
> > >>
> > >> We will migrate the existing PredictionIO mailing lists.
> > >>
> > >> Git repository
> > >>
> > >> The PredictionIO team would like to use Git for source control, due
> to
> > >> our
> > >> current use of GitHub.
> > >>
> > >> git://git.apache.org/incubator-predictionio
> > >>
> > >> Documentation
> > >>
> > >> https://predictionio.incubator.apache.org/docs/
> > >>
> > >> JIRA instance
> > >>
> > >> PredictionIO currently uses the GitHub issue tracking system
> > associated
> > >> with its repository:
> > https://github.com/PredictionIO/PredictionIO/issues
> > >> .
> > >> We will migrate to Apache JIRA.
> > >>
> > >> JIRA PREDICTIONIO
> > >> https://issues.apache.org/jira/browse/PREDICTIONIO
> > >>
> > >> Other Resources
> > >>
> > >> TravisCI for builds and test running.
> > >>
> > >> PredictionIO's documentation, included in the code repo (docs/manual
> > >> directory), is built with Middleman and publicly hosted at
> > >> https://docs.prediction.io
> > >>
> > >> A blog to drive adoption and excitement at
> https://blog.prediction.io
> > >>
> > >> Initial Committers
> > >>
> > >> Pat Ferrell
> > >> Tamas Jambor
> > >> Justin Yip
> > >> Xusen Yin
> > >> Lee Moon Soo
> > >> Donald Szeto
> > >> Kenneth Chan
> > >> Tom Chan
> > >> Simon Chan
> > >> Marco Vivero
> > >> Matthew Tovbin
> > >> Yevgeny Khodorkovsky
> > >> Felipe Oliveira
> > >> Vitaly Gordon
> > >> Alex Merritt
> > >>
> > >> Affiliations
> > >>
> > >> Pat Ferrell - ActionML
> > >> Tamas Jambor - Channel4
> > >> Justin Yip - independent
> > >> Xusen Yin - USC
> > >> Lee Moon Soo - NFLabs
> > >> Donald Szeto - Salesforce
> > >> Kenneth Chan - Salesforce
> > >> Tom Chan - Salesforce
> > >> Simon Chan - Salesforce
> > >> Marco Vivero - Salesforce
> > >> Matthew Tovbin - Salesforce
> > >> Yevgeny Khodorkovsky - Salesforce
> > >> Felipe Oliveira - Salesforce
> > >> Vitaly Gordon - Salesforce
> > >> Alex Merritt - ActionML
> > >>
> > >> Sponsors
> > >>
> > >> Champion
> > >>
> > >> Andrew Purtell <apurtell at apache dot org>
> > >>
> > >> Nominated Mentors
> > >>
> > >> Andrew Purtell <apurtell at apache dot org>
> > >> James Taylor <jtaylor at apache dot org>
> > >> Lars Hofhansl <larsh at apache dot org>
> > >> Suneel Marthi <smarthi at apache dot org>
> > >> Xiangrui Meng <meng at apache dot org>
> > >> Luciano Resende <lresende at apache dot org>
> > >>
> > >> Sponsoring Entity
> > >>
> > >> Apache Incubator PMC
> > >>
> > >>
> > >> --
> > >> Best regards,
> > >>
> > >> - Andy
> > >>
> > >> Problems worthy of attack prove their worth by hitting back. - Piet
> Hein
> > >> (via Tom White)
> > >>
> >
> > ---------------------------------------------------------------------
> > To unsubscribe, e-mail: general-unsubscribe@incubator.apache.org
> > For additional commands, e-mail: general-help@incubator.apache.org
> >
> >
>
>
> --
> Paul Fremantle
> Co-Founder and CTO, WSO2
> Member of the Apache Software Foundation
> OASIS WS-RX TC Co-chair
>
> blog: http://pzf.fremantle.org
> twitter: @pzfreo
>
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Paul Fremantle <pz...@gmail.com>.
+1 (binding)
Paul
On Wed, May 25, 2016 at 5:12 AM, Tsuyoshi Ozawa <oz...@apache.org> wrote:
> +1 (non-binding)
> - Tsuyoshi
>
> On Wed, May 25, 2016 at 1:00 PM, Reynold Xin <rx...@databricks.com> wrote:
> > +1 (binding)
> >
> >
> > On Mon, May 23, 2016 at 3:22 PM, Andrew Purtell <ap...@apache.org>
> wrote:
> >
> >> Since discussion on the matter of PredictionIO has died down, I would
> like
> >> to call a VOTE
> >> on accepting PredictionIO into the Apache Incubator.
> >>
> >> Proposal: https://wiki.apache.org/incubator/PredictionIO
> >>
> >> [ ] +1 Accept PredictionIO into the Apache Incubator
> >> [ ] +0 Abstain
> >> [ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
> >>
> >> This vote will be open for at least 72 hours.
> >>
> >> My vote is +1 (binding)
> >>
> >> --
> >>
> >> PredictionIO Proposal
> >>
> >> Abstract
> >>
> >> PredictionIO is an open source Machine Learning Server built on top of
> >> state-of-the-art open source stack, that enables developers to manage
> and
> >> deploy production-ready predictive services for various kinds of machine
> >> learning tasks.
> >>
> >> Proposal
> >>
> >> The PredictionIO platform consists of the following components:
> >>
> >> * PredictionIO framework - provides the machine learning stack for
> >> building, evaluating and deploying engines with machine learning
> >> algorithms. It uses Apache Spark for processing.
> >>
> >> * Event Server - the machine learning analytics layer for unifying
> >> events
> >> from multiple platforms. It can use Apache HBase or any JDBC
> backends
> >> as its data store.
> >>
> >> The PredictionIO community also maintains a Template Gallery, a place to
> >> publish and download (free or proprietary) engine templates for
> different
> >> types of machine learning applications, and is a complemental part of
> the
> >> project. At this point we exclude the Template Gallery from the
> proposal,
> >> as it has a separate set of contributors and we’re not familiar with an
> >> Apache approved mechanism to maintain such a gallery.
> >>
> >> Background
> >>
> >> PredictionIO was started with a mission to democratize and bring machine
> >> learning to the masses.
> >>
> >> Machine learning has traditionally been a luxury for big companies like
> >> Google, Facebook, and Netflix. There are ML libraries and tools lying
> >> around the internet but the effort of putting them all together as a
> >> production-ready infrastructure is a very resource-intensive task that
> is
> >> remotely reachable by individuals or small businesses.
> >>
> >> PredictionIO is a production-ready, full stack machine learning system
> that
> >> allows organizations of any scale to quickly deploy machine learning
> >> capabilities. It comes with official and community-contributed machine
> >> learning engine templates that are easy to customize.
> >>
> >> Rationale
> >>
> >> As usage and number of contributors to PredictionIO has grown bigger and
> >> more diverse, we have sought for an independent framework for the
> project
> >> to keep thriving. We believe the Apache foundation is a great fit.
> Joining
> >> Apache would ensure that tried and true processes and procedures are in
> >> place for the growing number of organizations interested in contributing
> >> to PredictionIO. PredictionIO is also a good fit for the Apache
> foundation.
> >> PredictionIO was built on top of several Apache projects (HBase, Spark,
> >> Hadoop). We are familiar with the Apache process and believe that the
> >> democratic and meritocratic nature of the foundation aligns with the
> >> project goals.
> >>
> >> Initial Goals
> >>
> >> The initial milestones will be to move the existing codebase to Apache
> and
> >> integrate with the Apache development process. Once this is
> accomplished,
> >> we plan for incremental development and releases that follow the Apache
> >> guidelines, as well as growing our developer and user communities.
> >>
> >> Current Status
> >>
> >> PredictionIO has undergone nine minor releases and many patches.
> >> PredictionIO is being used in production by Salesforce.com as well as
> many
> >> other organizations and apps. The PredictionIO codebase is currently
> >> hosted at GitHub, which will form the basis of the Apache git
> repository.
> >>
> >> Meritocracy
> >>
> >> We plan to invest in supporting a meritocracy. We will discuss the
> >> requirements in an open forum. We intend to invite additional developers
> >> to participate. We will encourage and monitor community participation so
> >> that privileges can be extended to those that contribute.
> >>
> >> Community
> >>
> >> Acceptance into the Apache foundation would bolster the already strong
> >> user and developer community around PredictionIO. That community
> includes
> >> many contributors from various other companies, and an active mailing
> list
> >> composed of hundreds of users.
> >>
> >> Core Developers
> >>
> >> The core developers of our project are listed in our contributors and
> >> initial PPMC below. Though many are employed at Salesforce.com, there
> are
> >> also engineers from ActionML, and independent developers.
> >>
> >> Alignment
> >>
> >> The ASF is the natural choice to host the PredictionIO project as its
> goal
> >> is democratizing Machine Learning by making it more easily accessible to
> >> every user/developer. PredictionIO is built on top of several top level
> >> Apache projects as outlined above.
> >>
> >> Known Risks
> >>
> >> Orphaned Products
> >>
> >> PredictionIO has a solid and growing community. It is deployed on
> >> production environments by companies of all sizes to run various kinds
> of
> >> predictive engines.
> >>
> >> In addition to the community contribution to PredictionIO framework, the
> >> community is also actively contributing new engines to the Template
> >> Gallery as well as SDKs and documentation for the project. Salesforce is
> >> committed to utilize and advance the PredictionIO code base and support
> >> its user community.
> >>
> >> Inexperience with Open Source
> >>
> >> PredictionIO has existed as a healthy open source project for almost two
> >> years and is the most starred Scala project on GitHub. All of the
> proposed
> >> committers have contributed to ASF and Linux Foundation open source
> >> projects. Several current committers on Apache projects and Apache
> Members
> >> are involved in this proposal and intend to provide mentorship.
> >>
> >> Homogeneous Developers
> >>
> >> The initial list of committers includes developers from several
> >> institutions, including Salesforce, ActionML, Channel4, USC as well as
> >> unaffiliated developers.
> >>
> >> Reliance on Salaried Developers
> >>
> >> Like most open source projects, PredictionIO receives substantial
> support
> >> from salaried developers. PredictionIO development is partially
> supported
> >> by Salesforce.com, but there are many contributors from various other
> >> companies, and an active mailing list composed of hundreds of users. We
> >> will continue our efforts to ensure stewardship of the project to be
> >> independent of salaried developers by meritocratically promoting those
> >> contributors to committers.
> >>
> >> Relationships with Other Apache Product
> >>
> >> PredictionIO relies heavily on top level Apache projects such as Apache
> >> Spark, HBase and Hadoop. However it brings a distinguished
> functionality,
> >> rather than just an abstraction - Machine Learning in a plug-and-play
> >> fashion.
> >>
> >> Compared to Apache Mahout, which focuses on the development of a wide
> >> variety of algorithms, PredictionIO offers a platform to manage the
> whole
> >> machine learning workflow, including data collection, data preparation,
> >> modeling, deployment and management of predictive services in production
> >> environments.
> >>
> >> An Excessive Fascination with the Apache Brand
> >>
> >> PredictionIO is already a widely known open source project. This
> proposal
> >> is not for the purpose of generating publicity. Rather, the primary
> >> benefits to joining Apache are those outlined in the Rationale section.
> >>
> >> Documentation
> >>
> >> PredictionIO boasts rich and live documentation, included in the code
> repo
> >> (docs/manual directory), is built with Middleman, and publicly hosted at
> >> https://docs.prediction.io
> >>
> >> Initial Source and Intellectual Property Submission Plan
> >>
> >> Currently, the PredictionIO codebase is distributed under the Apache 2.0
> >> License and hosted on GitHub:
> https://github.com/PredictionIO/PredictionIO
> >>
> >> External Dependencies
> >>
> >> PredictionIO has the following external dependencies:
> >> * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are
> >> needed)
> >> * Apache Spark 1.3.0 for Hadoop 2.4
> >> * Java SE Development Kit 8
> >> * and one of the following sets:
> >> * PostgreSQL 9.1
> >> or
> >> * MySQL 5.1
> >> or
> >> * Apache HBase 0.98.6
> >> * Elasticsearch 1.4.0
> >>
> >> Upon acceptance to the incubator, we would begin a thorough analysis of
> >> all transitive dependencies to verify this information and introduce
> >> license checking into the build and release process by integrating with
> >> Apache RAT.
> >>
> >> Cryptography
> >>
> >> PredictionIO does not include cryptographic code. We utilize standard
> >> JCE and JSSE APIs provided by the Java Runtime Environment.
> >>
> >> Required Resources
> >>
> >> We request that following resources be created for the project to use
> >>
> >> Mailing lists
> >>
> >> predictionio-private@incubator.apache.org (with moderated
> subscriptions)
> >> predictionio-dev
> >> predictionio-user
> >> predictionio-commits
> >>
> >> We will migrate the existing PredictionIO mailing lists.
> >>
> >> Git repository
> >>
> >> The PredictionIO team would like to use Git for source control, due to
> >> our
> >> current use of GitHub.
> >>
> >> git://git.apache.org/incubator-predictionio
> >>
> >> Documentation
> >>
> >> https://predictionio.incubator.apache.org/docs/
> >>
> >> JIRA instance
> >>
> >> PredictionIO currently uses the GitHub issue tracking system
> associated
> >> with its repository:
> https://github.com/PredictionIO/PredictionIO/issues
> >> .
> >> We will migrate to Apache JIRA.
> >>
> >> JIRA PREDICTIONIO
> >> https://issues.apache.org/jira/browse/PREDICTIONIO
> >>
> >> Other Resources
> >>
> >> TravisCI for builds and test running.
> >>
> >> PredictionIO's documentation, included in the code repo (docs/manual
> >> directory), is built with Middleman and publicly hosted at
> >> https://docs.prediction.io
> >>
> >> A blog to drive adoption and excitement at https://blog.prediction.io
> >>
> >> Initial Committers
> >>
> >> Pat Ferrell
> >> Tamas Jambor
> >> Justin Yip
> >> Xusen Yin
> >> Lee Moon Soo
> >> Donald Szeto
> >> Kenneth Chan
> >> Tom Chan
> >> Simon Chan
> >> Marco Vivero
> >> Matthew Tovbin
> >> Yevgeny Khodorkovsky
> >> Felipe Oliveira
> >> Vitaly Gordon
> >> Alex Merritt
> >>
> >> Affiliations
> >>
> >> Pat Ferrell - ActionML
> >> Tamas Jambor - Channel4
> >> Justin Yip - independent
> >> Xusen Yin - USC
> >> Lee Moon Soo - NFLabs
> >> Donald Szeto - Salesforce
> >> Kenneth Chan - Salesforce
> >> Tom Chan - Salesforce
> >> Simon Chan - Salesforce
> >> Marco Vivero - Salesforce
> >> Matthew Tovbin - Salesforce
> >> Yevgeny Khodorkovsky - Salesforce
> >> Felipe Oliveira - Salesforce
> >> Vitaly Gordon - Salesforce
> >> Alex Merritt - ActionML
> >>
> >> Sponsors
> >>
> >> Champion
> >>
> >> Andrew Purtell <apurtell at apache dot org>
> >>
> >> Nominated Mentors
> >>
> >> Andrew Purtell <apurtell at apache dot org>
> >> James Taylor <jtaylor at apache dot org>
> >> Lars Hofhansl <larsh at apache dot org>
> >> Suneel Marthi <smarthi at apache dot org>
> >> Xiangrui Meng <meng at apache dot org>
> >> Luciano Resende <lresende at apache dot org>
> >>
> >> Sponsoring Entity
> >>
> >> Apache Incubator PMC
> >>
> >>
> >> --
> >> Best regards,
> >>
> >> - Andy
> >>
> >> Problems worthy of attack prove their worth by hitting back. - Piet Hein
> >> (via Tom White)
> >>
>
> ---------------------------------------------------------------------
> To unsubscribe, e-mail: general-unsubscribe@incubator.apache.org
> For additional commands, e-mail: general-help@incubator.apache.org
>
>
--
Paul Fremantle
Co-Founder and CTO, WSO2
Member of the Apache Software Foundation
OASIS WS-RX TC Co-chair
blog: http://pzf.fremantle.org
twitter: @pzfreo
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Tsuyoshi Ozawa <oz...@apache.org>.
+1 (non-binding)
- Tsuyoshi
On Wed, May 25, 2016 at 1:00 PM, Reynold Xin <rx...@databricks.com> wrote:
> +1 (binding)
>
>
> On Mon, May 23, 2016 at 3:22 PM, Andrew Purtell <ap...@apache.org> wrote:
>
>> Since discussion on the matter of PredictionIO has died down, I would like
>> to call a VOTE
>> on accepting PredictionIO into the Apache Incubator.
>>
>> Proposal: https://wiki.apache.org/incubator/PredictionIO
>>
>> [ ] +1 Accept PredictionIO into the Apache Incubator
>> [ ] +0 Abstain
>> [ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
>>
>> This vote will be open for at least 72 hours.
>>
>> My vote is +1 (binding)
>>
>> --
>>
>> PredictionIO Proposal
>>
>> Abstract
>>
>> PredictionIO is an open source Machine Learning Server built on top of
>> state-of-the-art open source stack, that enables developers to manage and
>> deploy production-ready predictive services for various kinds of machine
>> learning tasks.
>>
>> Proposal
>>
>> The PredictionIO platform consists of the following components:
>>
>> * PredictionIO framework - provides the machine learning stack for
>> building, evaluating and deploying engines with machine learning
>> algorithms. It uses Apache Spark for processing.
>>
>> * Event Server - the machine learning analytics layer for unifying
>> events
>> from multiple platforms. It can use Apache HBase or any JDBC backends
>> as its data store.
>>
>> The PredictionIO community also maintains a Template Gallery, a place to
>> publish and download (free or proprietary) engine templates for different
>> types of machine learning applications, and is a complemental part of the
>> project. At this point we exclude the Template Gallery from the proposal,
>> as it has a separate set of contributors and we’re not familiar with an
>> Apache approved mechanism to maintain such a gallery.
>>
>> Background
>>
>> PredictionIO was started with a mission to democratize and bring machine
>> learning to the masses.
>>
>> Machine learning has traditionally been a luxury for big companies like
>> Google, Facebook, and Netflix. There are ML libraries and tools lying
>> around the internet but the effort of putting them all together as a
>> production-ready infrastructure is a very resource-intensive task that is
>> remotely reachable by individuals or small businesses.
>>
>> PredictionIO is a production-ready, full stack machine learning system that
>> allows organizations of any scale to quickly deploy machine learning
>> capabilities. It comes with official and community-contributed machine
>> learning engine templates that are easy to customize.
>>
>> Rationale
>>
>> As usage and number of contributors to PredictionIO has grown bigger and
>> more diverse, we have sought for an independent framework for the project
>> to keep thriving. We believe the Apache foundation is a great fit. Joining
>> Apache would ensure that tried and true processes and procedures are in
>> place for the growing number of organizations interested in contributing
>> to PredictionIO. PredictionIO is also a good fit for the Apache foundation.
>> PredictionIO was built on top of several Apache projects (HBase, Spark,
>> Hadoop). We are familiar with the Apache process and believe that the
>> democratic and meritocratic nature of the foundation aligns with the
>> project goals.
>>
>> Initial Goals
>>
>> The initial milestones will be to move the existing codebase to Apache and
>> integrate with the Apache development process. Once this is accomplished,
>> we plan for incremental development and releases that follow the Apache
>> guidelines, as well as growing our developer and user communities.
>>
>> Current Status
>>
>> PredictionIO has undergone nine minor releases and many patches.
>> PredictionIO is being used in production by Salesforce.com as well as many
>> other organizations and apps. The PredictionIO codebase is currently
>> hosted at GitHub, which will form the basis of the Apache git repository.
>>
>> Meritocracy
>>
>> We plan to invest in supporting a meritocracy. We will discuss the
>> requirements in an open forum. We intend to invite additional developers
>> to participate. We will encourage and monitor community participation so
>> that privileges can be extended to those that contribute.
>>
>> Community
>>
>> Acceptance into the Apache foundation would bolster the already strong
>> user and developer community around PredictionIO. That community includes
>> many contributors from various other companies, and an active mailing list
>> composed of hundreds of users.
>>
>> Core Developers
>>
>> The core developers of our project are listed in our contributors and
>> initial PPMC below. Though many are employed at Salesforce.com, there are
>> also engineers from ActionML, and independent developers.
>>
>> Alignment
>>
>> The ASF is the natural choice to host the PredictionIO project as its goal
>> is democratizing Machine Learning by making it more easily accessible to
>> every user/developer. PredictionIO is built on top of several top level
>> Apache projects as outlined above.
>>
>> Known Risks
>>
>> Orphaned Products
>>
>> PredictionIO has a solid and growing community. It is deployed on
>> production environments by companies of all sizes to run various kinds of
>> predictive engines.
>>
>> In addition to the community contribution to PredictionIO framework, the
>> community is also actively contributing new engines to the Template
>> Gallery as well as SDKs and documentation for the project. Salesforce is
>> committed to utilize and advance the PredictionIO code base and support
>> its user community.
>>
>> Inexperience with Open Source
>>
>> PredictionIO has existed as a healthy open source project for almost two
>> years and is the most starred Scala project on GitHub. All of the proposed
>> committers have contributed to ASF and Linux Foundation open source
>> projects. Several current committers on Apache projects and Apache Members
>> are involved in this proposal and intend to provide mentorship.
>>
>> Homogeneous Developers
>>
>> The initial list of committers includes developers from several
>> institutions, including Salesforce, ActionML, Channel4, USC as well as
>> unaffiliated developers.
>>
>> Reliance on Salaried Developers
>>
>> Like most open source projects, PredictionIO receives substantial support
>> from salaried developers. PredictionIO development is partially supported
>> by Salesforce.com, but there are many contributors from various other
>> companies, and an active mailing list composed of hundreds of users. We
>> will continue our efforts to ensure stewardship of the project to be
>> independent of salaried developers by meritocratically promoting those
>> contributors to committers.
>>
>> Relationships with Other Apache Product
>>
>> PredictionIO relies heavily on top level Apache projects such as Apache
>> Spark, HBase and Hadoop. However it brings a distinguished functionality,
>> rather than just an abstraction - Machine Learning in a plug-and-play
>> fashion.
>>
>> Compared to Apache Mahout, which focuses on the development of a wide
>> variety of algorithms, PredictionIO offers a platform to manage the whole
>> machine learning workflow, including data collection, data preparation,
>> modeling, deployment and management of predictive services in production
>> environments.
>>
>> An Excessive Fascination with the Apache Brand
>>
>> PredictionIO is already a widely known open source project. This proposal
>> is not for the purpose of generating publicity. Rather, the primary
>> benefits to joining Apache are those outlined in the Rationale section.
>>
>> Documentation
>>
>> PredictionIO boasts rich and live documentation, included in the code repo
>> (docs/manual directory), is built with Middleman, and publicly hosted at
>> https://docs.prediction.io
>>
>> Initial Source and Intellectual Property Submission Plan
>>
>> Currently, the PredictionIO codebase is distributed under the Apache 2.0
>> License and hosted on GitHub: https://github.com/PredictionIO/PredictionIO
>>
>> External Dependencies
>>
>> PredictionIO has the following external dependencies:
>> * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are
>> needed)
>> * Apache Spark 1.3.0 for Hadoop 2.4
>> * Java SE Development Kit 8
>> * and one of the following sets:
>> * PostgreSQL 9.1
>> or
>> * MySQL 5.1
>> or
>> * Apache HBase 0.98.6
>> * Elasticsearch 1.4.0
>>
>> Upon acceptance to the incubator, we would begin a thorough analysis of
>> all transitive dependencies to verify this information and introduce
>> license checking into the build and release process by integrating with
>> Apache RAT.
>>
>> Cryptography
>>
>> PredictionIO does not include cryptographic code. We utilize standard
>> JCE and JSSE APIs provided by the Java Runtime Environment.
>>
>> Required Resources
>>
>> We request that following resources be created for the project to use
>>
>> Mailing lists
>>
>> predictionio-private@incubator.apache.org (with moderated subscriptions)
>> predictionio-dev
>> predictionio-user
>> predictionio-commits
>>
>> We will migrate the existing PredictionIO mailing lists.
>>
>> Git repository
>>
>> The PredictionIO team would like to use Git for source control, due to
>> our
>> current use of GitHub.
>>
>> git://git.apache.org/incubator-predictionio
>>
>> Documentation
>>
>> https://predictionio.incubator.apache.org/docs/
>>
>> JIRA instance
>>
>> PredictionIO currently uses the GitHub issue tracking system associated
>> with its repository: https://github.com/PredictionIO/PredictionIO/issues
>> .
>> We will migrate to Apache JIRA.
>>
>> JIRA PREDICTIONIO
>> https://issues.apache.org/jira/browse/PREDICTIONIO
>>
>> Other Resources
>>
>> TravisCI for builds and test running.
>>
>> PredictionIO's documentation, included in the code repo (docs/manual
>> directory), is built with Middleman and publicly hosted at
>> https://docs.prediction.io
>>
>> A blog to drive adoption and excitement at https://blog.prediction.io
>>
>> Initial Committers
>>
>> Pat Ferrell
>> Tamas Jambor
>> Justin Yip
>> Xusen Yin
>> Lee Moon Soo
>> Donald Szeto
>> Kenneth Chan
>> Tom Chan
>> Simon Chan
>> Marco Vivero
>> Matthew Tovbin
>> Yevgeny Khodorkovsky
>> Felipe Oliveira
>> Vitaly Gordon
>> Alex Merritt
>>
>> Affiliations
>>
>> Pat Ferrell - ActionML
>> Tamas Jambor - Channel4
>> Justin Yip - independent
>> Xusen Yin - USC
>> Lee Moon Soo - NFLabs
>> Donald Szeto - Salesforce
>> Kenneth Chan - Salesforce
>> Tom Chan - Salesforce
>> Simon Chan - Salesforce
>> Marco Vivero - Salesforce
>> Matthew Tovbin - Salesforce
>> Yevgeny Khodorkovsky - Salesforce
>> Felipe Oliveira - Salesforce
>> Vitaly Gordon - Salesforce
>> Alex Merritt - ActionML
>>
>> Sponsors
>>
>> Champion
>>
>> Andrew Purtell <apurtell at apache dot org>
>>
>> Nominated Mentors
>>
>> Andrew Purtell <apurtell at apache dot org>
>> James Taylor <jtaylor at apache dot org>
>> Lars Hofhansl <larsh at apache dot org>
>> Suneel Marthi <smarthi at apache dot org>
>> Xiangrui Meng <meng at apache dot org>
>> Luciano Resende <lresende at apache dot org>
>>
>> Sponsoring Entity
>>
>> Apache Incubator PMC
>>
>>
>> --
>> Best regards,
>>
>> - Andy
>>
>> Problems worthy of attack prove their worth by hitting back. - Piet Hein
>> (via Tom White)
>>
---------------------------------------------------------------------
To unsubscribe, e-mail: general-unsubscribe@incubator.apache.org
For additional commands, e-mail: general-help@incubator.apache.org
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Reynold Xin <rx...@databricks.com>.
+1 (binding)
On Mon, May 23, 2016 at 3:22 PM, Andrew Purtell <ap...@apache.org> wrote:
> Since discussion on the matter of PredictionIO has died down, I would like
> to call a VOTE
> on accepting PredictionIO into the Apache Incubator.
>
> Proposal: https://wiki.apache.org/incubator/PredictionIO
>
> [ ] +1 Accept PredictionIO into the Apache Incubator
> [ ] +0 Abstain
> [ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
>
> This vote will be open for at least 72 hours.
>
> My vote is +1 (binding)
>
> --
>
> PredictionIO Proposal
>
> Abstract
>
> PredictionIO is an open source Machine Learning Server built on top of
> state-of-the-art open source stack, that enables developers to manage and
> deploy production-ready predictive services for various kinds of machine
> learning tasks.
>
> Proposal
>
> The PredictionIO platform consists of the following components:
>
> * PredictionIO framework - provides the machine learning stack for
> building, evaluating and deploying engines with machine learning
> algorithms. It uses Apache Spark for processing.
>
> * Event Server - the machine learning analytics layer for unifying
> events
> from multiple platforms. It can use Apache HBase or any JDBC backends
> as its data store.
>
> The PredictionIO community also maintains a Template Gallery, a place to
> publish and download (free or proprietary) engine templates for different
> types of machine learning applications, and is a complemental part of the
> project. At this point we exclude the Template Gallery from the proposal,
> as it has a separate set of contributors and we’re not familiar with an
> Apache approved mechanism to maintain such a gallery.
>
> Background
>
> PredictionIO was started with a mission to democratize and bring machine
> learning to the masses.
>
> Machine learning has traditionally been a luxury for big companies like
> Google, Facebook, and Netflix. There are ML libraries and tools lying
> around the internet but the effort of putting them all together as a
> production-ready infrastructure is a very resource-intensive task that is
> remotely reachable by individuals or small businesses.
>
> PredictionIO is a production-ready, full stack machine learning system that
> allows organizations of any scale to quickly deploy machine learning
> capabilities. It comes with official and community-contributed machine
> learning engine templates that are easy to customize.
>
> Rationale
>
> As usage and number of contributors to PredictionIO has grown bigger and
> more diverse, we have sought for an independent framework for the project
> to keep thriving. We believe the Apache foundation is a great fit. Joining
> Apache would ensure that tried and true processes and procedures are in
> place for the growing number of organizations interested in contributing
> to PredictionIO. PredictionIO is also a good fit for the Apache foundation.
> PredictionIO was built on top of several Apache projects (HBase, Spark,
> Hadoop). We are familiar with the Apache process and believe that the
> democratic and meritocratic nature of the foundation aligns with the
> project goals.
>
> Initial Goals
>
> The initial milestones will be to move the existing codebase to Apache and
> integrate with the Apache development process. Once this is accomplished,
> we plan for incremental development and releases that follow the Apache
> guidelines, as well as growing our developer and user communities.
>
> Current Status
>
> PredictionIO has undergone nine minor releases and many patches.
> PredictionIO is being used in production by Salesforce.com as well as many
> other organizations and apps. The PredictionIO codebase is currently
> hosted at GitHub, which will form the basis of the Apache git repository.
>
> Meritocracy
>
> We plan to invest in supporting a meritocracy. We will discuss the
> requirements in an open forum. We intend to invite additional developers
> to participate. We will encourage and monitor community participation so
> that privileges can be extended to those that contribute.
>
> Community
>
> Acceptance into the Apache foundation would bolster the already strong
> user and developer community around PredictionIO. That community includes
> many contributors from various other companies, and an active mailing list
> composed of hundreds of users.
>
> Core Developers
>
> The core developers of our project are listed in our contributors and
> initial PPMC below. Though many are employed at Salesforce.com, there are
> also engineers from ActionML, and independent developers.
>
> Alignment
>
> The ASF is the natural choice to host the PredictionIO project as its goal
> is democratizing Machine Learning by making it more easily accessible to
> every user/developer. PredictionIO is built on top of several top level
> Apache projects as outlined above.
>
> Known Risks
>
> Orphaned Products
>
> PredictionIO has a solid and growing community. It is deployed on
> production environments by companies of all sizes to run various kinds of
> predictive engines.
>
> In addition to the community contribution to PredictionIO framework, the
> community is also actively contributing new engines to the Template
> Gallery as well as SDKs and documentation for the project. Salesforce is
> committed to utilize and advance the PredictionIO code base and support
> its user community.
>
> Inexperience with Open Source
>
> PredictionIO has existed as a healthy open source project for almost two
> years and is the most starred Scala project on GitHub. All of the proposed
> committers have contributed to ASF and Linux Foundation open source
> projects. Several current committers on Apache projects and Apache Members
> are involved in this proposal and intend to provide mentorship.
>
> Homogeneous Developers
>
> The initial list of committers includes developers from several
> institutions, including Salesforce, ActionML, Channel4, USC as well as
> unaffiliated developers.
>
> Reliance on Salaried Developers
>
> Like most open source projects, PredictionIO receives substantial support
> from salaried developers. PredictionIO development is partially supported
> by Salesforce.com, but there are many contributors from various other
> companies, and an active mailing list composed of hundreds of users. We
> will continue our efforts to ensure stewardship of the project to be
> independent of salaried developers by meritocratically promoting those
> contributors to committers.
>
> Relationships with Other Apache Product
>
> PredictionIO relies heavily on top level Apache projects such as Apache
> Spark, HBase and Hadoop. However it brings a distinguished functionality,
> rather than just an abstraction - Machine Learning in a plug-and-play
> fashion.
>
> Compared to Apache Mahout, which focuses on the development of a wide
> variety of algorithms, PredictionIO offers a platform to manage the whole
> machine learning workflow, including data collection, data preparation,
> modeling, deployment and management of predictive services in production
> environments.
>
> An Excessive Fascination with the Apache Brand
>
> PredictionIO is already a widely known open source project. This proposal
> is not for the purpose of generating publicity. Rather, the primary
> benefits to joining Apache are those outlined in the Rationale section.
>
> Documentation
>
> PredictionIO boasts rich and live documentation, included in the code repo
> (docs/manual directory), is built with Middleman, and publicly hosted at
> https://docs.prediction.io
>
> Initial Source and Intellectual Property Submission Plan
>
> Currently, the PredictionIO codebase is distributed under the Apache 2.0
> License and hosted on GitHub: https://github.com/PredictionIO/PredictionIO
>
> External Dependencies
>
> PredictionIO has the following external dependencies:
> * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are
> needed)
> * Apache Spark 1.3.0 for Hadoop 2.4
> * Java SE Development Kit 8
> * and one of the following sets:
> * PostgreSQL 9.1
> or
> * MySQL 5.1
> or
> * Apache HBase 0.98.6
> * Elasticsearch 1.4.0
>
> Upon acceptance to the incubator, we would begin a thorough analysis of
> all transitive dependencies to verify this information and introduce
> license checking into the build and release process by integrating with
> Apache RAT.
>
> Cryptography
>
> PredictionIO does not include cryptographic code. We utilize standard
> JCE and JSSE APIs provided by the Java Runtime Environment.
>
> Required Resources
>
> We request that following resources be created for the project to use
>
> Mailing lists
>
> predictionio-private@incubator.apache.org (with moderated subscriptions)
> predictionio-dev
> predictionio-user
> predictionio-commits
>
> We will migrate the existing PredictionIO mailing lists.
>
> Git repository
>
> The PredictionIO team would like to use Git for source control, due to
> our
> current use of GitHub.
>
> git://git.apache.org/incubator-predictionio
>
> Documentation
>
> https://predictionio.incubator.apache.org/docs/
>
> JIRA instance
>
> PredictionIO currently uses the GitHub issue tracking system associated
> with its repository: https://github.com/PredictionIO/PredictionIO/issues
> .
> We will migrate to Apache JIRA.
>
> JIRA PREDICTIONIO
> https://issues.apache.org/jira/browse/PREDICTIONIO
>
> Other Resources
>
> TravisCI for builds and test running.
>
> PredictionIO's documentation, included in the code repo (docs/manual
> directory), is built with Middleman and publicly hosted at
> https://docs.prediction.io
>
> A blog to drive adoption and excitement at https://blog.prediction.io
>
> Initial Committers
>
> Pat Ferrell
> Tamas Jambor
> Justin Yip
> Xusen Yin
> Lee Moon Soo
> Donald Szeto
> Kenneth Chan
> Tom Chan
> Simon Chan
> Marco Vivero
> Matthew Tovbin
> Yevgeny Khodorkovsky
> Felipe Oliveira
> Vitaly Gordon
> Alex Merritt
>
> Affiliations
>
> Pat Ferrell - ActionML
> Tamas Jambor - Channel4
> Justin Yip - independent
> Xusen Yin - USC
> Lee Moon Soo - NFLabs
> Donald Szeto - Salesforce
> Kenneth Chan - Salesforce
> Tom Chan - Salesforce
> Simon Chan - Salesforce
> Marco Vivero - Salesforce
> Matthew Tovbin - Salesforce
> Yevgeny Khodorkovsky - Salesforce
> Felipe Oliveira - Salesforce
> Vitaly Gordon - Salesforce
> Alex Merritt - ActionML
>
> Sponsors
>
> Champion
>
> Andrew Purtell <apurtell at apache dot org>
>
> Nominated Mentors
>
> Andrew Purtell <apurtell at apache dot org>
> James Taylor <jtaylor at apache dot org>
> Lars Hofhansl <larsh at apache dot org>
> Suneel Marthi <smarthi at apache dot org>
> Xiangrui Meng <meng at apache dot org>
> Luciano Resende <lresende at apache dot org>
>
> Sponsoring Entity
>
> Apache Incubator PMC
>
>
> --
> Best regards,
>
> - Andy
>
> Problems worthy of attack prove their worth by hitting back. - Piet Hein
> (via Tom White)
>
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Bertrand Delacretaz <bd...@apache.org>.
On Tue, May 24, 2016 at 12:22 AM, Andrew Purtell <ap...@apache.org> wrote:
> Since discussion on the matter of PredictionIO has died down, I would like
> to call a VOTE
> on accepting PredictionIO into the Apache Incubator...
+1
-Bertrand
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Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Sergio Fernández <wi...@apache.org>.
+1 (binding)
On Tue, May 24, 2016 at 12:22 AM, Andrew Purtell <ap...@apache.org>
wrote:
> Since discussion on the matter of PredictionIO has died down, I would like
> to call a VOTE
> on accepting PredictionIO into the Apache Incubator.
>
> Proposal: https://wiki.apache.org/incubator/PredictionIO
>
> [ ] +1 Accept PredictionIO into the Apache Incubator
> [ ] +0 Abstain
> [ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
>
> This vote will be open for at least 72 hours.
>
> My vote is +1 (binding)
>
> --
>
> PredictionIO Proposal
>
> Abstract
>
> PredictionIO is an open source Machine Learning Server built on top of
> state-of-the-art open source stack, that enables developers to manage and
> deploy production-ready predictive services for various kinds of machine
> learning tasks.
>
> Proposal
>
> The PredictionIO platform consists of the following components:
>
> * PredictionIO framework - provides the machine learning stack for
> building, evaluating and deploying engines with machine learning
> algorithms. It uses Apache Spark for processing.
>
> * Event Server - the machine learning analytics layer for unifying
> events
> from multiple platforms. It can use Apache HBase or any JDBC backends
> as its data store.
>
> The PredictionIO community also maintains a Template Gallery, a place to
> publish and download (free or proprietary) engine templates for different
> types of machine learning applications, and is a complemental part of the
> project. At this point we exclude the Template Gallery from the proposal,
> as it has a separate set of contributors and we’re not familiar with an
> Apache approved mechanism to maintain such a gallery.
>
> Background
>
> PredictionIO was started with a mission to democratize and bring machine
> learning to the masses.
>
> Machine learning has traditionally been a luxury for big companies like
> Google, Facebook, and Netflix. There are ML libraries and tools lying
> around the internet but the effort of putting them all together as a
> production-ready infrastructure is a very resource-intensive task that is
> remotely reachable by individuals or small businesses.
>
> PredictionIO is a production-ready, full stack machine learning system that
> allows organizations of any scale to quickly deploy machine learning
> capabilities. It comes with official and community-contributed machine
> learning engine templates that are easy to customize.
>
> Rationale
>
> As usage and number of contributors to PredictionIO has grown bigger and
> more diverse, we have sought for an independent framework for the project
> to keep thriving. We believe the Apache foundation is a great fit. Joining
> Apache would ensure that tried and true processes and procedures are in
> place for the growing number of organizations interested in contributing
> to PredictionIO. PredictionIO is also a good fit for the Apache foundation.
> PredictionIO was built on top of several Apache projects (HBase, Spark,
> Hadoop). We are familiar with the Apache process and believe that the
> democratic and meritocratic nature of the foundation aligns with the
> project goals.
>
> Initial Goals
>
> The initial milestones will be to move the existing codebase to Apache and
> integrate with the Apache development process. Once this is accomplished,
> we plan for incremental development and releases that follow the Apache
> guidelines, as well as growing our developer and user communities.
>
> Current Status
>
> PredictionIO has undergone nine minor releases and many patches.
> PredictionIO is being used in production by Salesforce.com as well as many
> other organizations and apps. The PredictionIO codebase is currently
> hosted at GitHub, which will form the basis of the Apache git repository.
>
> Meritocracy
>
> We plan to invest in supporting a meritocracy. We will discuss the
> requirements in an open forum. We intend to invite additional developers
> to participate. We will encourage and monitor community participation so
> that privileges can be extended to those that contribute.
>
> Community
>
> Acceptance into the Apache foundation would bolster the already strong
> user and developer community around PredictionIO. That community includes
> many contributors from various other companies, and an active mailing list
> composed of hundreds of users.
>
> Core Developers
>
> The core developers of our project are listed in our contributors and
> initial PPMC below. Though many are employed at Salesforce.com, there are
> also engineers from ActionML, and independent developers.
>
> Alignment
>
> The ASF is the natural choice to host the PredictionIO project as its goal
> is democratizing Machine Learning by making it more easily accessible to
> every user/developer. PredictionIO is built on top of several top level
> Apache projects as outlined above.
>
> Known Risks
>
> Orphaned Products
>
> PredictionIO has a solid and growing community. It is deployed on
> production environments by companies of all sizes to run various kinds of
> predictive engines.
>
> In addition to the community contribution to PredictionIO framework, the
> community is also actively contributing new engines to the Template
> Gallery as well as SDKs and documentation for the project. Salesforce is
> committed to utilize and advance the PredictionIO code base and support
> its user community.
>
> Inexperience with Open Source
>
> PredictionIO has existed as a healthy open source project for almost two
> years and is the most starred Scala project on GitHub. All of the proposed
> committers have contributed to ASF and Linux Foundation open source
> projects. Several current committers on Apache projects and Apache Members
> are involved in this proposal and intend to provide mentorship.
>
> Homogeneous Developers
>
> The initial list of committers includes developers from several
> institutions, including Salesforce, ActionML, Channel4, USC as well as
> unaffiliated developers.
>
> Reliance on Salaried Developers
>
> Like most open source projects, PredictionIO receives substantial support
> from salaried developers. PredictionIO development is partially supported
> by Salesforce.com, but there are many contributors from various other
> companies, and an active mailing list composed of hundreds of users. We
> will continue our efforts to ensure stewardship of the project to be
> independent of salaried developers by meritocratically promoting those
> contributors to committers.
>
> Relationships with Other Apache Product
>
> PredictionIO relies heavily on top level Apache projects such as Apache
> Spark, HBase and Hadoop. However it brings a distinguished functionality,
> rather than just an abstraction - Machine Learning in a plug-and-play
> fashion.
>
> Compared to Apache Mahout, which focuses on the development of a wide
> variety of algorithms, PredictionIO offers a platform to manage the whole
> machine learning workflow, including data collection, data preparation,
> modeling, deployment and management of predictive services in production
> environments.
>
> An Excessive Fascination with the Apache Brand
>
> PredictionIO is already a widely known open source project. This proposal
> is not for the purpose of generating publicity. Rather, the primary
> benefits to joining Apache are those outlined in the Rationale section.
>
> Documentation
>
> PredictionIO boasts rich and live documentation, included in the code repo
> (docs/manual directory), is built with Middleman, and publicly hosted at
> https://docs.prediction.io
>
> Initial Source and Intellectual Property Submission Plan
>
> Currently, the PredictionIO codebase is distributed under the Apache 2.0
> License and hosted on GitHub: https://github.com/PredictionIO/PredictionIO
>
> External Dependencies
>
> PredictionIO has the following external dependencies:
> * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are
> needed)
> * Apache Spark 1.3.0 for Hadoop 2.4
> * Java SE Development Kit 8
> * and one of the following sets:
> * PostgreSQL 9.1
> or
> * MySQL 5.1
> or
> * Apache HBase 0.98.6
> * Elasticsearch 1.4.0
>
> Upon acceptance to the incubator, we would begin a thorough analysis of
> all transitive dependencies to verify this information and introduce
> license checking into the build and release process by integrating with
> Apache RAT.
>
> Cryptography
>
> PredictionIO does not include cryptographic code. We utilize standard
> JCE and JSSE APIs provided by the Java Runtime Environment.
>
> Required Resources
>
> We request that following resources be created for the project to use
>
> Mailing lists
>
> predictionio-private@incubator.apache.org (with moderated subscriptions)
> predictionio-dev
> predictionio-user
> predictionio-commits
>
> We will migrate the existing PredictionIO mailing lists.
>
> Git repository
>
> The PredictionIO team would like to use Git for source control, due to
> our
> current use of GitHub.
>
> git://git.apache.org/incubator-predictionio
>
> Documentation
>
> https://predictionio.incubator.apache.org/docs/
>
> JIRA instance
>
> PredictionIO currently uses the GitHub issue tracking system associated
> with its repository: https://github.com/PredictionIO/PredictionIO/issues
> .
> We will migrate to Apache JIRA.
>
> JIRA PREDICTIONIO
> https://issues.apache.org/jira/browse/PREDICTIONIO
>
> Other Resources
>
> TravisCI for builds and test running.
>
> PredictionIO's documentation, included in the code repo (docs/manual
> directory), is built with Middleman and publicly hosted at
> https://docs.prediction.io
>
> A blog to drive adoption and excitement at https://blog.prediction.io
>
> Initial Committers
>
> Pat Ferrell
> Tamas Jambor
> Justin Yip
> Xusen Yin
> Lee Moon Soo
> Donald Szeto
> Kenneth Chan
> Tom Chan
> Simon Chan
> Marco Vivero
> Matthew Tovbin
> Yevgeny Khodorkovsky
> Felipe Oliveira
> Vitaly Gordon
> Alex Merritt
>
> Affiliations
>
> Pat Ferrell - ActionML
> Tamas Jambor - Channel4
> Justin Yip - independent
> Xusen Yin - USC
> Lee Moon Soo - NFLabs
> Donald Szeto - Salesforce
> Kenneth Chan - Salesforce
> Tom Chan - Salesforce
> Simon Chan - Salesforce
> Marco Vivero - Salesforce
> Matthew Tovbin - Salesforce
> Yevgeny Khodorkovsky - Salesforce
> Felipe Oliveira - Salesforce
> Vitaly Gordon - Salesforce
> Alex Merritt - ActionML
>
> Sponsors
>
> Champion
>
> Andrew Purtell <apurtell at apache dot org>
>
> Nominated Mentors
>
> Andrew Purtell <apurtell at apache dot org>
> James Taylor <jtaylor at apache dot org>
> Lars Hofhansl <larsh at apache dot org>
> Suneel Marthi <smarthi at apache dot org>
> Xiangrui Meng <meng at apache dot org>
> Luciano Resende <lresende at apache dot org>
>
> Sponsoring Entity
>
> Apache Incubator PMC
>
>
> --
> Best regards,
>
> - Andy
>
> Problems worthy of attack prove their worth by hitting back. - Piet Hein
> (via Tom White)
>
--
Sergio Fernández
Partner Technology Manager
Redlink GmbH
m: +43 6602747925
e: sergio.fernandez@redlink.co
w: http://redlink.co
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Hitesh Shah <hi...@apache.org>.
+1 (binding)
— Hitesh
> On May 23, 2016, at 3:22 PM, Andrew Purtell <ap...@apache.org> wrote:
>
> Since discussion on the matter of PredictionIO has died down, I would like
> to call a VOTE
> on accepting PredictionIO into the Apache Incubator.
>
> Proposal: https://wiki.apache.org/incubator/PredictionIO
>
> [ ] +1 Accept PredictionIO into the Apache Incubator
> [ ] +0 Abstain
> [ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
>
> This vote will be open for at least 72 hours.
>
> My vote is +1 (binding)
>
> --
>
> PredictionIO Proposal
>
> Abstract
>
> PredictionIO is an open source Machine Learning Server built on top of
> state-of-the-art open source stack, that enables developers to manage and
> deploy production-ready predictive services for various kinds of machine
> learning tasks.
>
> Proposal
>
> The PredictionIO platform consists of the following components:
>
> * PredictionIO framework - provides the machine learning stack for
> building, evaluating and deploying engines with machine learning
> algorithms. It uses Apache Spark for processing.
>
> * Event Server - the machine learning analytics layer for unifying events
> from multiple platforms. It can use Apache HBase or any JDBC backends
> as its data store.
>
> The PredictionIO community also maintains a Template Gallery, a place to
> publish and download (free or proprietary) engine templates for different
> types of machine learning applications, and is a complemental part of the
> project. At this point we exclude the Template Gallery from the proposal,
> as it has a separate set of contributors and we’re not familiar with an
> Apache approved mechanism to maintain such a gallery.
>
> Background
>
> PredictionIO was started with a mission to democratize and bring machine
> learning to the masses.
>
> Machine learning has traditionally been a luxury for big companies like
> Google, Facebook, and Netflix. There are ML libraries and tools lying
> around the internet but the effort of putting them all together as a
> production-ready infrastructure is a very resource-intensive task that is
> remotely reachable by individuals or small businesses.
>
> PredictionIO is a production-ready, full stack machine learning system that
> allows organizations of any scale to quickly deploy machine learning
> capabilities. It comes with official and community-contributed machine
> learning engine templates that are easy to customize.
>
> Rationale
>
> As usage and number of contributors to PredictionIO has grown bigger and
> more diverse, we have sought for an independent framework for the project
> to keep thriving. We believe the Apache foundation is a great fit. Joining
> Apache would ensure that tried and true processes and procedures are in
> place for the growing number of organizations interested in contributing
> to PredictionIO. PredictionIO is also a good fit for the Apache foundation.
> PredictionIO was built on top of several Apache projects (HBase, Spark,
> Hadoop). We are familiar with the Apache process and believe that the
> democratic and meritocratic nature of the foundation aligns with the
> project goals.
>
> Initial Goals
>
> The initial milestones will be to move the existing codebase to Apache and
> integrate with the Apache development process. Once this is accomplished,
> we plan for incremental development and releases that follow the Apache
> guidelines, as well as growing our developer and user communities.
>
> Current Status
>
> PredictionIO has undergone nine minor releases and many patches.
> PredictionIO is being used in production by Salesforce.com as well as many
> other organizations and apps. The PredictionIO codebase is currently
> hosted at GitHub, which will form the basis of the Apache git repository.
>
> Meritocracy
>
> We plan to invest in supporting a meritocracy. We will discuss the
> requirements in an open forum. We intend to invite additional developers
> to participate. We will encourage and monitor community participation so
> that privileges can be extended to those that contribute.
>
> Community
>
> Acceptance into the Apache foundation would bolster the already strong
> user and developer community around PredictionIO. That community includes
> many contributors from various other companies, and an active mailing list
> composed of hundreds of users.
>
> Core Developers
>
> The core developers of our project are listed in our contributors and
> initial PPMC below. Though many are employed at Salesforce.com, there are
> also engineers from ActionML, and independent developers.
>
> Alignment
>
> The ASF is the natural choice to host the PredictionIO project as its goal
> is democratizing Machine Learning by making it more easily accessible to
> every user/developer. PredictionIO is built on top of several top level
> Apache projects as outlined above.
>
> Known Risks
>
> Orphaned Products
>
> PredictionIO has a solid and growing community. It is deployed on
> production environments by companies of all sizes to run various kinds of
> predictive engines.
>
> In addition to the community contribution to PredictionIO framework, the
> community is also actively contributing new engines to the Template
> Gallery as well as SDKs and documentation for the project. Salesforce is
> committed to utilize and advance the PredictionIO code base and support
> its user community.
>
> Inexperience with Open Source
>
> PredictionIO has existed as a healthy open source project for almost two
> years and is the most starred Scala project on GitHub. All of the proposed
> committers have contributed to ASF and Linux Foundation open source
> projects. Several current committers on Apache projects and Apache Members
> are involved in this proposal and intend to provide mentorship.
>
> Homogeneous Developers
>
> The initial list of committers includes developers from several
> institutions, including Salesforce, ActionML, Channel4, USC as well as
> unaffiliated developers.
>
> Reliance on Salaried Developers
>
> Like most open source projects, PredictionIO receives substantial support
> from salaried developers. PredictionIO development is partially supported
> by Salesforce.com, but there are many contributors from various other
> companies, and an active mailing list composed of hundreds of users. We
> will continue our efforts to ensure stewardship of the project to be
> independent of salaried developers by meritocratically promoting those
> contributors to committers.
>
> Relationships with Other Apache Product
>
> PredictionIO relies heavily on top level Apache projects such as Apache
> Spark, HBase and Hadoop. However it brings a distinguished functionality,
> rather than just an abstraction - Machine Learning in a plug-and-play
> fashion.
>
> Compared to Apache Mahout, which focuses on the development of a wide
> variety of algorithms, PredictionIO offers a platform to manage the whole
> machine learning workflow, including data collection, data preparation,
> modeling, deployment and management of predictive services in production
> environments.
>
> An Excessive Fascination with the Apache Brand
>
> PredictionIO is already a widely known open source project. This proposal
> is not for the purpose of generating publicity. Rather, the primary
> benefits to joining Apache are those outlined in the Rationale section.
>
> Documentation
>
> PredictionIO boasts rich and live documentation, included in the code repo
> (docs/manual directory), is built with Middleman, and publicly hosted at
> https://docs.prediction.io
>
> Initial Source and Intellectual Property Submission Plan
>
> Currently, the PredictionIO codebase is distributed under the Apache 2.0
> License and hosted on GitHub: https://github.com/PredictionIO/PredictionIO
>
> External Dependencies
>
> PredictionIO has the following external dependencies:
> * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are needed)
> * Apache Spark 1.3.0 for Hadoop 2.4
> * Java SE Development Kit 8
> * and one of the following sets:
> * PostgreSQL 9.1
> or
> * MySQL 5.1
> or
> * Apache HBase 0.98.6
> * Elasticsearch 1.4.0
>
> Upon acceptance to the incubator, we would begin a thorough analysis of
> all transitive dependencies to verify this information and introduce
> license checking into the build and release process by integrating with
> Apache RAT.
>
> Cryptography
>
> PredictionIO does not include cryptographic code. We utilize standard
> JCE and JSSE APIs provided by the Java Runtime Environment.
>
> Required Resources
>
> We request that following resources be created for the project to use
>
> Mailing lists
>
> predictionio-private@incubator.apache.org (with moderated subscriptions)
> predictionio-dev
> predictionio-user
> predictionio-commits
>
> We will migrate the existing PredictionIO mailing lists.
>
> Git repository
>
> The PredictionIO team would like to use Git for source control, due to our
> current use of GitHub.
>
> git://git.apache.org/incubator-predictionio
>
> Documentation
>
> https://predictionio.incubator.apache.org/docs/
>
> JIRA instance
>
> PredictionIO currently uses the GitHub issue tracking system associated
> with its repository: https://github.com/PredictionIO/PredictionIO/issues.
> We will migrate to Apache JIRA.
>
> JIRA PREDICTIONIO
> https://issues.apache.org/jira/browse/PREDICTIONIO
>
> Other Resources
>
> TravisCI for builds and test running.
>
> PredictionIO's documentation, included in the code repo (docs/manual
> directory), is built with Middleman and publicly hosted at
> https://docs.prediction.io
>
> A blog to drive adoption and excitement at https://blog.prediction.io
>
> Initial Committers
>
> Pat Ferrell
> Tamas Jambor
> Justin Yip
> Xusen Yin
> Lee Moon Soo
> Donald Szeto
> Kenneth Chan
> Tom Chan
> Simon Chan
> Marco Vivero
> Matthew Tovbin
> Yevgeny Khodorkovsky
> Felipe Oliveira
> Vitaly Gordon
> Alex Merritt
>
> Affiliations
>
> Pat Ferrell - ActionML
> Tamas Jambor - Channel4
> Justin Yip - independent
> Xusen Yin - USC
> Lee Moon Soo - NFLabs
> Donald Szeto - Salesforce
> Kenneth Chan - Salesforce
> Tom Chan - Salesforce
> Simon Chan - Salesforce
> Marco Vivero - Salesforce
> Matthew Tovbin - Salesforce
> Yevgeny Khodorkovsky - Salesforce
> Felipe Oliveira - Salesforce
> Vitaly Gordon - Salesforce
> Alex Merritt - ActionML
>
> Sponsors
>
> Champion
>
> Andrew Purtell <apurtell at apache dot org>
>
> Nominated Mentors
>
> Andrew Purtell <apurtell at apache dot org>
> James Taylor <jtaylor at apache dot org>
> Lars Hofhansl <larsh at apache dot org>
> Suneel Marthi <smarthi at apache dot org>
> Xiangrui Meng <meng at apache dot org>
> Luciano Resende <lresende at apache dot org>
>
> Sponsoring Entity
>
> Apache Incubator PMC
>
>
> --
> Best regards,
>
> - Andy
>
> Problems worthy of attack prove their worth by hitting back. - Piet Hein
> (via Tom White)
---------------------------------------------------------------------
To unsubscribe, e-mail: general-unsubscribe@incubator.apache.org
For additional commands, e-mail: general-help@incubator.apache.org
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Chris Nauroth <cn...@hortonworks.com>.
+1 (binding)
--Chris Nauroth
On 5/23/16, 3:22 PM, "Andrew Purtell" <ap...@apache.org> wrote:
>Since discussion on the matter of PredictionIO has died down, I would like
>to call a VOTE
>on accepting PredictionIO into the Apache Incubator.
>
>Proposal: https://wiki.apache.org/incubator/PredictionIO
>
>[ ] +1 Accept PredictionIO into the Apache Incubator
>[ ] +0 Abstain
>[ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
>
>This vote will be open for at least 72 hours.
>
>My vote is +1 (binding)
>
>--
>
>PredictionIO Proposal
>
>Abstract
>
>PredictionIO is an open source Machine Learning Server built on top of
>state-of-the-art open source stack, that enables developers to manage and
>deploy production-ready predictive services for various kinds of machine
>learning tasks.
>
>Proposal
>
>The PredictionIO platform consists of the following components:
>
> * PredictionIO framework - provides the machine learning stack for
> building, evaluating and deploying engines with machine learning
> algorithms. It uses Apache Spark for processing.
>
> * Event Server - the machine learning analytics layer for unifying
>events
> from multiple platforms. It can use Apache HBase or any JDBC backends
> as its data store.
>
>The PredictionIO community also maintains a Template Gallery, a place to
>publish and download (free or proprietary) engine templates for different
>types of machine learning applications, and is a complemental part of the
>project. At this point we exclude the Template Gallery from the proposal,
>as it has a separate set of contributors and we’re not familiar with an
>Apache approved mechanism to maintain such a gallery.
>
>Background
>
>PredictionIO was started with a mission to democratize and bring machine
>learning to the masses.
>
>Machine learning has traditionally been a luxury for big companies like
>Google, Facebook, and Netflix. There are ML libraries and tools lying
>around the internet but the effort of putting them all together as a
>production-ready infrastructure is a very resource-intensive task that is
>remotely reachable by individuals or small businesses.
>
>PredictionIO is a production-ready, full stack machine learning system
>that
>allows organizations of any scale to quickly deploy machine learning
>capabilities. It comes with official and community-contributed machine
>learning engine templates that are easy to customize.
>
>Rationale
>
>As usage and number of contributors to PredictionIO has grown bigger and
>more diverse, we have sought for an independent framework for the project
>to keep thriving. We believe the Apache foundation is a great fit. Joining
>Apache would ensure that tried and true processes and procedures are in
>place for the growing number of organizations interested in contributing
>to PredictionIO. PredictionIO is also a good fit for the Apache
>foundation.
>PredictionIO was built on top of several Apache projects (HBase, Spark,
>Hadoop). We are familiar with the Apache process and believe that the
>democratic and meritocratic nature of the foundation aligns with the
>project goals.
>
>Initial Goals
>
>The initial milestones will be to move the existing codebase to Apache and
>integrate with the Apache development process. Once this is accomplished,
>we plan for incremental development and releases that follow the Apache
>guidelines, as well as growing our developer and user communities.
>
>Current Status
>
>PredictionIO has undergone nine minor releases and many patches.
>PredictionIO is being used in production by Salesforce.com as well as many
>other organizations and apps. The PredictionIO codebase is currently
>hosted at GitHub, which will form the basis of the Apache git repository.
>
>Meritocracy
>
>We plan to invest in supporting a meritocracy. We will discuss the
>requirements in an open forum. We intend to invite additional developers
>to participate. We will encourage and monitor community participation so
>that privileges can be extended to those that contribute.
>
>Community
>
>Acceptance into the Apache foundation would bolster the already strong
>user and developer community around PredictionIO. That community includes
>many contributors from various other companies, and an active mailing list
>composed of hundreds of users.
>
>Core Developers
>
>The core developers of our project are listed in our contributors and
>initial PPMC below. Though many are employed at Salesforce.com, there are
>also engineers from ActionML, and independent developers.
>
>Alignment
>
>The ASF is the natural choice to host the PredictionIO project as its goal
>is democratizing Machine Learning by making it more easily accessible to
>every user/developer. PredictionIO is built on top of several top level
>Apache projects as outlined above.
>
>Known Risks
>
>Orphaned Products
>
>PredictionIO has a solid and growing community. It is deployed on
>production environments by companies of all sizes to run various kinds of
>predictive engines.
>
>In addition to the community contribution to PredictionIO framework, the
>community is also actively contributing new engines to the Template
>Gallery as well as SDKs and documentation for the project. Salesforce is
>committed to utilize and advance the PredictionIO code base and support
>its user community.
>
>Inexperience with Open Source
>
>PredictionIO has existed as a healthy open source project for almost two
>years and is the most starred Scala project on GitHub. All of the proposed
>committers have contributed to ASF and Linux Foundation open source
>projects. Several current committers on Apache projects and Apache Members
>are involved in this proposal and intend to provide mentorship.
>
>Homogeneous Developers
>
>The initial list of committers includes developers from several
>institutions, including Salesforce, ActionML, Channel4, USC as well as
>unaffiliated developers.
>
>Reliance on Salaried Developers
>
>Like most open source projects, PredictionIO receives substantial support
>from salaried developers. PredictionIO development is partially supported
>by Salesforce.com, but there are many contributors from various other
>companies, and an active mailing list composed of hundreds of users. We
>will continue our efforts to ensure stewardship of the project to be
>independent of salaried developers by meritocratically promoting those
>contributors to committers.
>
>Relationships with Other Apache Product
>
>PredictionIO relies heavily on top level Apache projects such as Apache
>Spark, HBase and Hadoop. However it brings a distinguished functionality,
>rather than just an abstraction - Machine Learning in a plug-and-play
>fashion.
>
>Compared to Apache Mahout, which focuses on the development of a wide
>variety of algorithms, PredictionIO offers a platform to manage the whole
>machine learning workflow, including data collection, data preparation,
>modeling, deployment and management of predictive services in production
>environments.
>
>An Excessive Fascination with the Apache Brand
>
>PredictionIO is already a widely known open source project. This proposal
>is not for the purpose of generating publicity. Rather, the primary
>benefits to joining Apache are those outlined in the Rationale section.
>
>Documentation
>
>PredictionIO boasts rich and live documentation, included in the code repo
>(docs/manual directory), is built with Middleman, and publicly hosted at
>https://docs.prediction.io
>
>Initial Source and Intellectual Property Submission Plan
>
>Currently, the PredictionIO codebase is distributed under the Apache 2.0
>License and hosted on GitHub: https://github.com/PredictionIO/PredictionIO
>
>External Dependencies
>
>PredictionIO has the following external dependencies:
> * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are
>needed)
> * Apache Spark 1.3.0 for Hadoop 2.4
> * Java SE Development Kit 8
> * and one of the following sets:
> * PostgreSQL 9.1
> or
> * MySQL 5.1
> or
> * Apache HBase 0.98.6
> * Elasticsearch 1.4.0
>
>Upon acceptance to the incubator, we would begin a thorough analysis of
>all transitive dependencies to verify this information and introduce
>license checking into the build and release process by integrating with
>Apache RAT.
>
>Cryptography
>
>PredictionIO does not include cryptographic code. We utilize standard
>JCE and JSSE APIs provided by the Java Runtime Environment.
>
>Required Resources
>
>We request that following resources be created for the project to use
>
>Mailing lists
>
> predictionio-private@incubator.apache.org (with moderated subscriptions)
> predictionio-dev
> predictionio-user
> predictionio-commits
>
> We will migrate the existing PredictionIO mailing lists.
>
>Git repository
>
> The PredictionIO team would like to use Git for source control, due to
>our
> current use of GitHub.
>
> git://git.apache.org/incubator-predictionio
>
>Documentation
>
> https://predictionio.incubator.apache.org/docs/
>
>JIRA instance
>
> PredictionIO currently uses the GitHub issue tracking system associated
> with its repository:
>https://github.com/PredictionIO/PredictionIO/issues.
> We will migrate to Apache JIRA.
>
> JIRA PREDICTIONIO
> https://issues.apache.org/jira/browse/PREDICTIONIO
>
>Other Resources
>
> TravisCI for builds and test running.
>
> PredictionIO's documentation, included in the code repo (docs/manual
> directory), is built with Middleman and publicly hosted at
> https://docs.prediction.io
>
> A blog to drive adoption and excitement at https://blog.prediction.io
>
>Initial Committers
>
> Pat Ferrell
> Tamas Jambor
> Justin Yip
> Xusen Yin
> Lee Moon Soo
> Donald Szeto
> Kenneth Chan
> Tom Chan
> Simon Chan
> Marco Vivero
> Matthew Tovbin
> Yevgeny Khodorkovsky
> Felipe Oliveira
> Vitaly Gordon
> Alex Merritt
>
>Affiliations
>
> Pat Ferrell - ActionML
> Tamas Jambor - Channel4
> Justin Yip - independent
> Xusen Yin - USC
> Lee Moon Soo - NFLabs
> Donald Szeto - Salesforce
> Kenneth Chan - Salesforce
> Tom Chan - Salesforce
> Simon Chan - Salesforce
> Marco Vivero - Salesforce
> Matthew Tovbin - Salesforce
> Yevgeny Khodorkovsky - Salesforce
> Felipe Oliveira - Salesforce
> Vitaly Gordon - Salesforce
> Alex Merritt - ActionML
>
>Sponsors
>
>Champion
>
> Andrew Purtell <apurtell at apache dot org>
>
>Nominated Mentors
>
> Andrew Purtell <apurtell at apache dot org>
> James Taylor <jtaylor at apache dot org>
> Lars Hofhansl <larsh at apache dot org>
> Suneel Marthi <smarthi at apache dot org>
> Xiangrui Meng <meng at apache dot org>
> Luciano Resende <lresende at apache dot org>
>
>Sponsoring Entity
>
> Apache Incubator PMC
>
>
>--
>Best regards,
>
> - Andy
>
>Problems worthy of attack prove their worth by hitting back. - Piet Hein
>(via Tom White)
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Suneel Marthi <sm...@apache.org>.
+1 (binding)
On Mon, May 23, 2016 at 6:32 PM, Luciano Resende <lu...@gmail.com>
wrote:
> +1 (binding)
>
> On Mon, May 23, 2016 at 3:22 PM, Andrew Purtell <ap...@apache.org>
> wrote:
>
> > Since discussion on the matter of PredictionIO has died down, I would
> like
> > to call a VOTE
> > on accepting PredictionIO into the Apache Incubator.
> >
> > Proposal: https://wiki.apache.org/incubator/PredictionIO
> >
> > [ ] +1 Accept PredictionIO into the Apache Incubator
> > [ ] +0 Abstain
> > [ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
> >
> > This vote will be open for at least 72 hours.
> >
> > My vote is +1 (binding)
> >
> > --
> >
> > PredictionIO Proposal
> >
> > Abstract
> >
> > PredictionIO is an open source Machine Learning Server built on top of
> > state-of-the-art open source stack, that enables developers to manage and
> > deploy production-ready predictive services for various kinds of machine
> > learning tasks.
> >
> > Proposal
> >
> > The PredictionIO platform consists of the following components:
> >
> > * PredictionIO framework - provides the machine learning stack for
> > building, evaluating and deploying engines with machine learning
> > algorithms. It uses Apache Spark for processing.
> >
> > * Event Server - the machine learning analytics layer for unifying
> > events
> > from multiple platforms. It can use Apache HBase or any JDBC
> backends
> > as its data store.
> >
> > The PredictionIO community also maintains a Template Gallery, a place to
> > publish and download (free or proprietary) engine templates for different
> > types of machine learning applications, and is a complemental part of the
> > project. At this point we exclude the Template Gallery from the proposal,
> > as it has a separate set of contributors and we’re not familiar with an
> > Apache approved mechanism to maintain such a gallery.
> >
> > Background
> >
> > PredictionIO was started with a mission to democratize and bring machine
> > learning to the masses.
> >
> > Machine learning has traditionally been a luxury for big companies like
> > Google, Facebook, and Netflix. There are ML libraries and tools lying
> > around the internet but the effort of putting them all together as a
> > production-ready infrastructure is a very resource-intensive task that is
> > remotely reachable by individuals or small businesses.
> >
> > PredictionIO is a production-ready, full stack machine learning system
> that
> > allows organizations of any scale to quickly deploy machine learning
> > capabilities. It comes with official and community-contributed machine
> > learning engine templates that are easy to customize.
> >
> > Rationale
> >
> > As usage and number of contributors to PredictionIO has grown bigger and
> > more diverse, we have sought for an independent framework for the project
> > to keep thriving. We believe the Apache foundation is a great fit.
> Joining
> > Apache would ensure that tried and true processes and procedures are in
> > place for the growing number of organizations interested in contributing
> > to PredictionIO. PredictionIO is also a good fit for the Apache
> foundation.
> > PredictionIO was built on top of several Apache projects (HBase, Spark,
> > Hadoop). We are familiar with the Apache process and believe that the
> > democratic and meritocratic nature of the foundation aligns with the
> > project goals.
> >
> > Initial Goals
> >
> > The initial milestones will be to move the existing codebase to Apache
> and
> > integrate with the Apache development process. Once this is accomplished,
> > we plan for incremental development and releases that follow the Apache
> > guidelines, as well as growing our developer and user communities.
> >
> > Current Status
> >
> > PredictionIO has undergone nine minor releases and many patches.
> > PredictionIO is being used in production by Salesforce.com as well as
> many
> > other organizations and apps. The PredictionIO codebase is currently
> > hosted at GitHub, which will form the basis of the Apache git repository.
> >
> > Meritocracy
> >
> > We plan to invest in supporting a meritocracy. We will discuss the
> > requirements in an open forum. We intend to invite additional developers
> > to participate. We will encourage and monitor community participation so
> > that privileges can be extended to those that contribute.
> >
> > Community
> >
> > Acceptance into the Apache foundation would bolster the already strong
> > user and developer community around PredictionIO. That community includes
> > many contributors from various other companies, and an active mailing
> list
> > composed of hundreds of users.
> >
> > Core Developers
> >
> > The core developers of our project are listed in our contributors and
> > initial PPMC below. Though many are employed at Salesforce.com, there are
> > also engineers from ActionML, and independent developers.
> >
> > Alignment
> >
> > The ASF is the natural choice to host the PredictionIO project as its
> goal
> > is democratizing Machine Learning by making it more easily accessible to
> > every user/developer. PredictionIO is built on top of several top level
> > Apache projects as outlined above.
> >
> > Known Risks
> >
> > Orphaned Products
> >
> > PredictionIO has a solid and growing community. It is deployed on
> > production environments by companies of all sizes to run various kinds of
> > predictive engines.
> >
> > In addition to the community contribution to PredictionIO framework, the
> > community is also actively contributing new engines to the Template
> > Gallery as well as SDKs and documentation for the project. Salesforce is
> > committed to utilize and advance the PredictionIO code base and support
> > its user community.
> >
> > Inexperience with Open Source
> >
> > PredictionIO has existed as a healthy open source project for almost two
> > years and is the most starred Scala project on GitHub. All of the
> proposed
> > committers have contributed to ASF and Linux Foundation open source
> > projects. Several current committers on Apache projects and Apache
> Members
> > are involved in this proposal and intend to provide mentorship.
> >
> > Homogeneous Developers
> >
> > The initial list of committers includes developers from several
> > institutions, including Salesforce, ActionML, Channel4, USC as well as
> > unaffiliated developers.
> >
> > Reliance on Salaried Developers
> >
> > Like most open source projects, PredictionIO receives substantial support
> > from salaried developers. PredictionIO development is partially supported
> > by Salesforce.com, but there are many contributors from various other
> > companies, and an active mailing list composed of hundreds of users. We
> > will continue our efforts to ensure stewardship of the project to be
> > independent of salaried developers by meritocratically promoting those
> > contributors to committers.
> >
> > Relationships with Other Apache Product
> >
> > PredictionIO relies heavily on top level Apache projects such as Apache
> > Spark, HBase and Hadoop. However it brings a distinguished functionality,
> > rather than just an abstraction - Machine Learning in a plug-and-play
> > fashion.
> >
> > Compared to Apache Mahout, which focuses on the development of a wide
> > variety of algorithms, PredictionIO offers a platform to manage the whole
> > machine learning workflow, including data collection, data preparation,
> > modeling, deployment and management of predictive services in production
> > environments.
> >
> > An Excessive Fascination with the Apache Brand
> >
> > PredictionIO is already a widely known open source project. This proposal
> > is not for the purpose of generating publicity. Rather, the primary
> > benefits to joining Apache are those outlined in the Rationale section.
> >
> > Documentation
> >
> > PredictionIO boasts rich and live documentation, included in the code
> repo
> > (docs/manual directory), is built with Middleman, and publicly hosted at
> > https://docs.prediction.io
> >
> > Initial Source and Intellectual Property Submission Plan
> >
> > Currently, the PredictionIO codebase is distributed under the Apache 2.0
> > License and hosted on GitHub:
> https://github.com/PredictionIO/PredictionIO
> >
> > External Dependencies
> >
> > PredictionIO has the following external dependencies:
> > * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are
> > needed)
> > * Apache Spark 1.3.0 for Hadoop 2.4
> > * Java SE Development Kit 8
> > * and one of the following sets:
> > * PostgreSQL 9.1
> > or
> > * MySQL 5.1
> > or
> > * Apache HBase 0.98.6
> > * Elasticsearch 1.4.0
> >
> > Upon acceptance to the incubator, we would begin a thorough analysis of
> > all transitive dependencies to verify this information and introduce
> > license checking into the build and release process by integrating with
> > Apache RAT.
> >
> > Cryptography
> >
> > PredictionIO does not include cryptographic code. We utilize standard
> > JCE and JSSE APIs provided by the Java Runtime Environment.
> >
> > Required Resources
> >
> > We request that following resources be created for the project to use
> >
> > Mailing lists
> >
> > predictionio-private@incubator.apache.org (with moderated
> subscriptions)
> > predictionio-dev
> > predictionio-user
> > predictionio-commits
> >
> > We will migrate the existing PredictionIO mailing lists.
> >
> > Git repository
> >
> > The PredictionIO team would like to use Git for source control, due to
> > our
> > current use of GitHub.
> >
> > git://git.apache.org/incubator-predictionio
> >
> > Documentation
> >
> > https://predictionio.incubator.apache.org/docs/
> >
> > JIRA instance
> >
> > PredictionIO currently uses the GitHub issue tracking system associated
> > with its repository:
> https://github.com/PredictionIO/PredictionIO/issues
> > .
> > We will migrate to Apache JIRA.
> >
> > JIRA PREDICTIONIO
> > https://issues.apache.org/jira/browse/PREDICTIONIO
> >
> > Other Resources
> >
> > TravisCI for builds and test running.
> >
> > PredictionIO's documentation, included in the code repo (docs/manual
> > directory), is built with Middleman and publicly hosted at
> > https://docs.prediction.io
> >
> > A blog to drive adoption and excitement at https://blog.prediction.io
> >
> > Initial Committers
> >
> > Pat Ferrell
> > Tamas Jambor
> > Justin Yip
> > Xusen Yin
> > Lee Moon Soo
> > Donald Szeto
> > Kenneth Chan
> > Tom Chan
> > Simon Chan
> > Marco Vivero
> > Matthew Tovbin
> > Yevgeny Khodorkovsky
> > Felipe Oliveira
> > Vitaly Gordon
> > Alex Merritt
> >
> > Affiliations
> >
> > Pat Ferrell - ActionML
> > Tamas Jambor - Channel4
> > Justin Yip - independent
> > Xusen Yin - USC
> > Lee Moon Soo - NFLabs
> > Donald Szeto - Salesforce
> > Kenneth Chan - Salesforce
> > Tom Chan - Salesforce
> > Simon Chan - Salesforce
> > Marco Vivero - Salesforce
> > Matthew Tovbin - Salesforce
> > Yevgeny Khodorkovsky - Salesforce
> > Felipe Oliveira - Salesforce
> > Vitaly Gordon - Salesforce
> > Alex Merritt - ActionML
> >
> > Sponsors
> >
> > Champion
> >
> > Andrew Purtell <apurtell at apache dot org>
> >
> > Nominated Mentors
> >
> > Andrew Purtell <apurtell at apache dot org>
> > James Taylor <jtaylor at apache dot org>
> > Lars Hofhansl <larsh at apache dot org>
> > Suneel Marthi <smarthi at apache dot org>
> > Xiangrui Meng <meng at apache dot org>
> > Luciano Resende <lresende at apache dot org>
> >
> > Sponsoring Entity
> >
> > Apache Incubator PMC
> >
> >
> > --
> > Best regards,
> >
> > - Andy
> >
> > Problems worthy of attack prove their worth by hitting back. - Piet Hein
> > (via Tom White)
> >
>
>
>
> --
> Luciano Resende
> http://twitter.com/lresende1975
> http://lresende.blogspot.com/
>
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by James Taylor <ja...@apache.org>.
+1 (binding)
On Mon, May 23, 2016 at 3:32 PM, Luciano Resende <lu...@gmail.com>
wrote:
> +1 (binding)
>
> On Mon, May 23, 2016 at 3:22 PM, Andrew Purtell <ap...@apache.org>
> wrote:
>
> > Since discussion on the matter of PredictionIO has died down, I would
> like
> > to call a VOTE
> > on accepting PredictionIO into the Apache Incubator.
> >
> > Proposal: https://wiki.apache.org/incubator/PredictionIO
> >
> > [ ] +1 Accept PredictionIO into the Apache Incubator
> > [ ] +0 Abstain
> > [ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
> >
> > This vote will be open for at least 72 hours.
> >
> > My vote is +1 (binding)
> >
> > --
> >
> > PredictionIO Proposal
> >
> > Abstract
> >
> > PredictionIO is an open source Machine Learning Server built on top of
> > state-of-the-art open source stack, that enables developers to manage and
> > deploy production-ready predictive services for various kinds of machine
> > learning tasks.
> >
> > Proposal
> >
> > The PredictionIO platform consists of the following components:
> >
> > * PredictionIO framework - provides the machine learning stack for
> > building, evaluating and deploying engines with machine learning
> > algorithms. It uses Apache Spark for processing.
> >
> > * Event Server - the machine learning analytics layer for unifying
> > events
> > from multiple platforms. It can use Apache HBase or any JDBC
> backends
> > as its data store.
> >
> > The PredictionIO community also maintains a Template Gallery, a place to
> > publish and download (free or proprietary) engine templates for different
> > types of machine learning applications, and is a complemental part of the
> > project. At this point we exclude the Template Gallery from the proposal,
> > as it has a separate set of contributors and we’re not familiar with an
> > Apache approved mechanism to maintain such a gallery.
> >
> > Background
> >
> > PredictionIO was started with a mission to democratize and bring machine
> > learning to the masses.
> >
> > Machine learning has traditionally been a luxury for big companies like
> > Google, Facebook, and Netflix. There are ML libraries and tools lying
> > around the internet but the effort of putting them all together as a
> > production-ready infrastructure is a very resource-intensive task that is
> > remotely reachable by individuals or small businesses.
> >
> > PredictionIO is a production-ready, full stack machine learning system
> that
> > allows organizations of any scale to quickly deploy machine learning
> > capabilities. It comes with official and community-contributed machine
> > learning engine templates that are easy to customize.
> >
> > Rationale
> >
> > As usage and number of contributors to PredictionIO has grown bigger and
> > more diverse, we have sought for an independent framework for the project
> > to keep thriving. We believe the Apache foundation is a great fit.
> Joining
> > Apache would ensure that tried and true processes and procedures are in
> > place for the growing number of organizations interested in contributing
> > to PredictionIO. PredictionIO is also a good fit for the Apache
> foundation.
> > PredictionIO was built on top of several Apache projects (HBase, Spark,
> > Hadoop). We are familiar with the Apache process and believe that the
> > democratic and meritocratic nature of the foundation aligns with the
> > project goals.
> >
> > Initial Goals
> >
> > The initial milestones will be to move the existing codebase to Apache
> and
> > integrate with the Apache development process. Once this is accomplished,
> > we plan for incremental development and releases that follow the Apache
> > guidelines, as well as growing our developer and user communities.
> >
> > Current Status
> >
> > PredictionIO has undergone nine minor releases and many patches.
> > PredictionIO is being used in production by Salesforce.com as well as
> many
> > other organizations and apps. The PredictionIO codebase is currently
> > hosted at GitHub, which will form the basis of the Apache git repository.
> >
> > Meritocracy
> >
> > We plan to invest in supporting a meritocracy. We will discuss the
> > requirements in an open forum. We intend to invite additional developers
> > to participate. We will encourage and monitor community participation so
> > that privileges can be extended to those that contribute.
> >
> > Community
> >
> > Acceptance into the Apache foundation would bolster the already strong
> > user and developer community around PredictionIO. That community includes
> > many contributors from various other companies, and an active mailing
> list
> > composed of hundreds of users.
> >
> > Core Developers
> >
> > The core developers of our project are listed in our contributors and
> > initial PPMC below. Though many are employed at Salesforce.com, there are
> > also engineers from ActionML, and independent developers.
> >
> > Alignment
> >
> > The ASF is the natural choice to host the PredictionIO project as its
> goal
> > is democratizing Machine Learning by making it more easily accessible to
> > every user/developer. PredictionIO is built on top of several top level
> > Apache projects as outlined above.
> >
> > Known Risks
> >
> > Orphaned Products
> >
> > PredictionIO has a solid and growing community. It is deployed on
> > production environments by companies of all sizes to run various kinds of
> > predictive engines.
> >
> > In addition to the community contribution to PredictionIO framework, the
> > community is also actively contributing new engines to the Template
> > Gallery as well as SDKs and documentation for the project. Salesforce is
> > committed to utilize and advance the PredictionIO code base and support
> > its user community.
> >
> > Inexperience with Open Source
> >
> > PredictionIO has existed as a healthy open source project for almost two
> > years and is the most starred Scala project on GitHub. All of the
> proposed
> > committers have contributed to ASF and Linux Foundation open source
> > projects. Several current committers on Apache projects and Apache
> Members
> > are involved in this proposal and intend to provide mentorship.
> >
> > Homogeneous Developers
> >
> > The initial list of committers includes developers from several
> > institutions, including Salesforce, ActionML, Channel4, USC as well as
> > unaffiliated developers.
> >
> > Reliance on Salaried Developers
> >
> > Like most open source projects, PredictionIO receives substantial support
> > from salaried developers. PredictionIO development is partially supported
> > by Salesforce.com, but there are many contributors from various other
> > companies, and an active mailing list composed of hundreds of users. We
> > will continue our efforts to ensure stewardship of the project to be
> > independent of salaried developers by meritocratically promoting those
> > contributors to committers.
> >
> > Relationships with Other Apache Product
> >
> > PredictionIO relies heavily on top level Apache projects such as Apache
> > Spark, HBase and Hadoop. However it brings a distinguished functionality,
> > rather than just an abstraction - Machine Learning in a plug-and-play
> > fashion.
> >
> > Compared to Apache Mahout, which focuses on the development of a wide
> > variety of algorithms, PredictionIO offers a platform to manage the whole
> > machine learning workflow, including data collection, data preparation,
> > modeling, deployment and management of predictive services in production
> > environments.
> >
> > An Excessive Fascination with the Apache Brand
> >
> > PredictionIO is already a widely known open source project. This proposal
> > is not for the purpose of generating publicity. Rather, the primary
> > benefits to joining Apache are those outlined in the Rationale section.
> >
> > Documentation
> >
> > PredictionIO boasts rich and live documentation, included in the code
> repo
> > (docs/manual directory), is built with Middleman, and publicly hosted at
> > https://docs.prediction.io
> >
> > Initial Source and Intellectual Property Submission Plan
> >
> > Currently, the PredictionIO codebase is distributed under the Apache 2.0
> > License and hosted on GitHub:
> https://github.com/PredictionIO/PredictionIO
> >
> > External Dependencies
> >
> > PredictionIO has the following external dependencies:
> > * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are
> > needed)
> > * Apache Spark 1.3.0 for Hadoop 2.4
> > * Java SE Development Kit 8
> > * and one of the following sets:
> > * PostgreSQL 9.1
> > or
> > * MySQL 5.1
> > or
> > * Apache HBase 0.98.6
> > * Elasticsearch 1.4.0
> >
> > Upon acceptance to the incubator, we would begin a thorough analysis of
> > all transitive dependencies to verify this information and introduce
> > license checking into the build and release process by integrating with
> > Apache RAT.
> >
> > Cryptography
> >
> > PredictionIO does not include cryptographic code. We utilize standard
> > JCE and JSSE APIs provided by the Java Runtime Environment.
> >
> > Required Resources
> >
> > We request that following resources be created for the project to use
> >
> > Mailing lists
> >
> > predictionio-private@incubator.apache.org (with moderated
> subscriptions)
> > predictionio-dev
> > predictionio-user
> > predictionio-commits
> >
> > We will migrate the existing PredictionIO mailing lists.
> >
> > Git repository
> >
> > The PredictionIO team would like to use Git for source control, due to
> > our
> > current use of GitHub.
> >
> > git://git.apache.org/incubator-predictionio
> >
> > Documentation
> >
> > https://predictionio.incubator.apache.org/docs/
> >
> > JIRA instance
> >
> > PredictionIO currently uses the GitHub issue tracking system associated
> > with its repository:
> https://github.com/PredictionIO/PredictionIO/issues
> > .
> > We will migrate to Apache JIRA.
> >
> > JIRA PREDICTIONIO
> > https://issues.apache.org/jira/browse/PREDICTIONIO
> >
> > Other Resources
> >
> > TravisCI for builds and test running.
> >
> > PredictionIO's documentation, included in the code repo (docs/manual
> > directory), is built with Middleman and publicly hosted at
> > https://docs.prediction.io
> >
> > A blog to drive adoption and excitement at https://blog.prediction.io
> >
> > Initial Committers
> >
> > Pat Ferrell
> > Tamas Jambor
> > Justin Yip
> > Xusen Yin
> > Lee Moon Soo
> > Donald Szeto
> > Kenneth Chan
> > Tom Chan
> > Simon Chan
> > Marco Vivero
> > Matthew Tovbin
> > Yevgeny Khodorkovsky
> > Felipe Oliveira
> > Vitaly Gordon
> > Alex Merritt
> >
> > Affiliations
> >
> > Pat Ferrell - ActionML
> > Tamas Jambor - Channel4
> > Justin Yip - independent
> > Xusen Yin - USC
> > Lee Moon Soo - NFLabs
> > Donald Szeto - Salesforce
> > Kenneth Chan - Salesforce
> > Tom Chan - Salesforce
> > Simon Chan - Salesforce
> > Marco Vivero - Salesforce
> > Matthew Tovbin - Salesforce
> > Yevgeny Khodorkovsky - Salesforce
> > Felipe Oliveira - Salesforce
> > Vitaly Gordon - Salesforce
> > Alex Merritt - ActionML
> >
> > Sponsors
> >
> > Champion
> >
> > Andrew Purtell <apurtell at apache dot org>
> >
> > Nominated Mentors
> >
> > Andrew Purtell <apurtell at apache dot org>
> > James Taylor <jtaylor at apache dot org>
> > Lars Hofhansl <larsh at apache dot org>
> > Suneel Marthi <smarthi at apache dot org>
> > Xiangrui Meng <meng at apache dot org>
> > Luciano Resende <lresende at apache dot org>
> >
> > Sponsoring Entity
> >
> > Apache Incubator PMC
> >
> >
> > --
> > Best regards,
> >
> > - Andy
> >
> > Problems worthy of attack prove their worth by hitting back. - Piet Hein
> > (via Tom White)
> >
>
>
>
> --
> Luciano Resende
> http://twitter.com/lresende1975
> http://lresende.blogspot.com/
>
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Luciano Resende <lu...@gmail.com>.
+1 (binding)
On Mon, May 23, 2016 at 3:22 PM, Andrew Purtell <ap...@apache.org> wrote:
> Since discussion on the matter of PredictionIO has died down, I would like
> to call a VOTE
> on accepting PredictionIO into the Apache Incubator.
>
> Proposal: https://wiki.apache.org/incubator/PredictionIO
>
> [ ] +1 Accept PredictionIO into the Apache Incubator
> [ ] +0 Abstain
> [ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
>
> This vote will be open for at least 72 hours.
>
> My vote is +1 (binding)
>
> --
>
> PredictionIO Proposal
>
> Abstract
>
> PredictionIO is an open source Machine Learning Server built on top of
> state-of-the-art open source stack, that enables developers to manage and
> deploy production-ready predictive services for various kinds of machine
> learning tasks.
>
> Proposal
>
> The PredictionIO platform consists of the following components:
>
> * PredictionIO framework - provides the machine learning stack for
> building, evaluating and deploying engines with machine learning
> algorithms. It uses Apache Spark for processing.
>
> * Event Server - the machine learning analytics layer for unifying
> events
> from multiple platforms. It can use Apache HBase or any JDBC backends
> as its data store.
>
> The PredictionIO community also maintains a Template Gallery, a place to
> publish and download (free or proprietary) engine templates for different
> types of machine learning applications, and is a complemental part of the
> project. At this point we exclude the Template Gallery from the proposal,
> as it has a separate set of contributors and we’re not familiar with an
> Apache approved mechanism to maintain such a gallery.
>
> Background
>
> PredictionIO was started with a mission to democratize and bring machine
> learning to the masses.
>
> Machine learning has traditionally been a luxury for big companies like
> Google, Facebook, and Netflix. There are ML libraries and tools lying
> around the internet but the effort of putting them all together as a
> production-ready infrastructure is a very resource-intensive task that is
> remotely reachable by individuals or small businesses.
>
> PredictionIO is a production-ready, full stack machine learning system that
> allows organizations of any scale to quickly deploy machine learning
> capabilities. It comes with official and community-contributed machine
> learning engine templates that are easy to customize.
>
> Rationale
>
> As usage and number of contributors to PredictionIO has grown bigger and
> more diverse, we have sought for an independent framework for the project
> to keep thriving. We believe the Apache foundation is a great fit. Joining
> Apache would ensure that tried and true processes and procedures are in
> place for the growing number of organizations interested in contributing
> to PredictionIO. PredictionIO is also a good fit for the Apache foundation.
> PredictionIO was built on top of several Apache projects (HBase, Spark,
> Hadoop). We are familiar with the Apache process and believe that the
> democratic and meritocratic nature of the foundation aligns with the
> project goals.
>
> Initial Goals
>
> The initial milestones will be to move the existing codebase to Apache and
> integrate with the Apache development process. Once this is accomplished,
> we plan for incremental development and releases that follow the Apache
> guidelines, as well as growing our developer and user communities.
>
> Current Status
>
> PredictionIO has undergone nine minor releases and many patches.
> PredictionIO is being used in production by Salesforce.com as well as many
> other organizations and apps. The PredictionIO codebase is currently
> hosted at GitHub, which will form the basis of the Apache git repository.
>
> Meritocracy
>
> We plan to invest in supporting a meritocracy. We will discuss the
> requirements in an open forum. We intend to invite additional developers
> to participate. We will encourage and monitor community participation so
> that privileges can be extended to those that contribute.
>
> Community
>
> Acceptance into the Apache foundation would bolster the already strong
> user and developer community around PredictionIO. That community includes
> many contributors from various other companies, and an active mailing list
> composed of hundreds of users.
>
> Core Developers
>
> The core developers of our project are listed in our contributors and
> initial PPMC below. Though many are employed at Salesforce.com, there are
> also engineers from ActionML, and independent developers.
>
> Alignment
>
> The ASF is the natural choice to host the PredictionIO project as its goal
> is democratizing Machine Learning by making it more easily accessible to
> every user/developer. PredictionIO is built on top of several top level
> Apache projects as outlined above.
>
> Known Risks
>
> Orphaned Products
>
> PredictionIO has a solid and growing community. It is deployed on
> production environments by companies of all sizes to run various kinds of
> predictive engines.
>
> In addition to the community contribution to PredictionIO framework, the
> community is also actively contributing new engines to the Template
> Gallery as well as SDKs and documentation for the project. Salesforce is
> committed to utilize and advance the PredictionIO code base and support
> its user community.
>
> Inexperience with Open Source
>
> PredictionIO has existed as a healthy open source project for almost two
> years and is the most starred Scala project on GitHub. All of the proposed
> committers have contributed to ASF and Linux Foundation open source
> projects. Several current committers on Apache projects and Apache Members
> are involved in this proposal and intend to provide mentorship.
>
> Homogeneous Developers
>
> The initial list of committers includes developers from several
> institutions, including Salesforce, ActionML, Channel4, USC as well as
> unaffiliated developers.
>
> Reliance on Salaried Developers
>
> Like most open source projects, PredictionIO receives substantial support
> from salaried developers. PredictionIO development is partially supported
> by Salesforce.com, but there are many contributors from various other
> companies, and an active mailing list composed of hundreds of users. We
> will continue our efforts to ensure stewardship of the project to be
> independent of salaried developers by meritocratically promoting those
> contributors to committers.
>
> Relationships with Other Apache Product
>
> PredictionIO relies heavily on top level Apache projects such as Apache
> Spark, HBase and Hadoop. However it brings a distinguished functionality,
> rather than just an abstraction - Machine Learning in a plug-and-play
> fashion.
>
> Compared to Apache Mahout, which focuses on the development of a wide
> variety of algorithms, PredictionIO offers a platform to manage the whole
> machine learning workflow, including data collection, data preparation,
> modeling, deployment and management of predictive services in production
> environments.
>
> An Excessive Fascination with the Apache Brand
>
> PredictionIO is already a widely known open source project. This proposal
> is not for the purpose of generating publicity. Rather, the primary
> benefits to joining Apache are those outlined in the Rationale section.
>
> Documentation
>
> PredictionIO boasts rich and live documentation, included in the code repo
> (docs/manual directory), is built with Middleman, and publicly hosted at
> https://docs.prediction.io
>
> Initial Source and Intellectual Property Submission Plan
>
> Currently, the PredictionIO codebase is distributed under the Apache 2.0
> License and hosted on GitHub: https://github.com/PredictionIO/PredictionIO
>
> External Dependencies
>
> PredictionIO has the following external dependencies:
> * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are
> needed)
> * Apache Spark 1.3.0 for Hadoop 2.4
> * Java SE Development Kit 8
> * and one of the following sets:
> * PostgreSQL 9.1
> or
> * MySQL 5.1
> or
> * Apache HBase 0.98.6
> * Elasticsearch 1.4.0
>
> Upon acceptance to the incubator, we would begin a thorough analysis of
> all transitive dependencies to verify this information and introduce
> license checking into the build and release process by integrating with
> Apache RAT.
>
> Cryptography
>
> PredictionIO does not include cryptographic code. We utilize standard
> JCE and JSSE APIs provided by the Java Runtime Environment.
>
> Required Resources
>
> We request that following resources be created for the project to use
>
> Mailing lists
>
> predictionio-private@incubator.apache.org (with moderated subscriptions)
> predictionio-dev
> predictionio-user
> predictionio-commits
>
> We will migrate the existing PredictionIO mailing lists.
>
> Git repository
>
> The PredictionIO team would like to use Git for source control, due to
> our
> current use of GitHub.
>
> git://git.apache.org/incubator-predictionio
>
> Documentation
>
> https://predictionio.incubator.apache.org/docs/
>
> JIRA instance
>
> PredictionIO currently uses the GitHub issue tracking system associated
> with its repository: https://github.com/PredictionIO/PredictionIO/issues
> .
> We will migrate to Apache JIRA.
>
> JIRA PREDICTIONIO
> https://issues.apache.org/jira/browse/PREDICTIONIO
>
> Other Resources
>
> TravisCI for builds and test running.
>
> PredictionIO's documentation, included in the code repo (docs/manual
> directory), is built with Middleman and publicly hosted at
> https://docs.prediction.io
>
> A blog to drive adoption and excitement at https://blog.prediction.io
>
> Initial Committers
>
> Pat Ferrell
> Tamas Jambor
> Justin Yip
> Xusen Yin
> Lee Moon Soo
> Donald Szeto
> Kenneth Chan
> Tom Chan
> Simon Chan
> Marco Vivero
> Matthew Tovbin
> Yevgeny Khodorkovsky
> Felipe Oliveira
> Vitaly Gordon
> Alex Merritt
>
> Affiliations
>
> Pat Ferrell - ActionML
> Tamas Jambor - Channel4
> Justin Yip - independent
> Xusen Yin - USC
> Lee Moon Soo - NFLabs
> Donald Szeto - Salesforce
> Kenneth Chan - Salesforce
> Tom Chan - Salesforce
> Simon Chan - Salesforce
> Marco Vivero - Salesforce
> Matthew Tovbin - Salesforce
> Yevgeny Khodorkovsky - Salesforce
> Felipe Oliveira - Salesforce
> Vitaly Gordon - Salesforce
> Alex Merritt - ActionML
>
> Sponsors
>
> Champion
>
> Andrew Purtell <apurtell at apache dot org>
>
> Nominated Mentors
>
> Andrew Purtell <apurtell at apache dot org>
> James Taylor <jtaylor at apache dot org>
> Lars Hofhansl <larsh at apache dot org>
> Suneel Marthi <smarthi at apache dot org>
> Xiangrui Meng <meng at apache dot org>
> Luciano Resende <lresende at apache dot org>
>
> Sponsoring Entity
>
> Apache Incubator PMC
>
>
> --
> Best regards,
>
> - Andy
>
> Problems worthy of attack prove their worth by hitting back. - Piet Hein
> (via Tom White)
>
--
Luciano Resende
http://twitter.com/lresende1975
http://lresende.blogspot.com/
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Liang Chen <ch...@huawei.com>.
+ 1 (non-binding)
Regards
Liang
--
View this message in context: http://apache-incubator-general.996316.n3.nabble.com/VOTE-Accept-PredictionIO-into-the-Apache-Incubator-tp49739p49872.html
Sent from the Apache Incubator - General mailing list archive at Nabble.com.
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Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Drew Farris <dr...@apache.org>.
+1 (binding)
On Mon, May 23, 2016, 6:23 PM Andrew Purtell <ap...@apache.org> wrote:
> Since discussion on the matter of PredictionIO has died down, I would like
> to call a VOTE
> on accepting PredictionIO into the Apache Incubator.
>
> Proposal: https://wiki.apache.org/incubator/PredictionIO
>
> [ ] +1 Accept PredictionIO into the Apache Incubator
> [ ] +0 Abstain
> [ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
>
> This vote will be open for at least 72 hours.
>
> My vote is +1 (binding)
>
> --
>
> PredictionIO Proposal
>
> Abstract
>
> PredictionIO is an open source Machine Learning Server built on top of
> state-of-the-art open source stack, that enables developers to manage and
> deploy production-ready predictive services for various kinds of machine
> learning tasks.
>
> Proposal
>
> The PredictionIO platform consists of the following components:
>
> * PredictionIO framework - provides the machine learning stack for
> building, evaluating and deploying engines with machine learning
> algorithms. It uses Apache Spark for processing.
>
> * Event Server - the machine learning analytics layer for unifying
> events
> from multiple platforms. It can use Apache HBase or any JDBC backends
> as its data store.
>
> The PredictionIO community also maintains a Template Gallery, a place to
> publish and download (free or proprietary) engine templates for different
> types of machine learning applications, and is a complemental part of the
> project. At this point we exclude the Template Gallery from the proposal,
> as it has a separate set of contributors and we’re not familiar with an
> Apache approved mechanism to maintain such a gallery.
>
> Background
>
> PredictionIO was started with a mission to democratize and bring machine
> learning to the masses.
>
> Machine learning has traditionally been a luxury for big companies like
> Google, Facebook, and Netflix. There are ML libraries and tools lying
> around the internet but the effort of putting them all together as a
> production-ready infrastructure is a very resource-intensive task that is
> remotely reachable by individuals or small businesses.
>
> PredictionIO is a production-ready, full stack machine learning system that
> allows organizations of any scale to quickly deploy machine learning
> capabilities. It comes with official and community-contributed machine
> learning engine templates that are easy to customize.
>
> Rationale
>
> As usage and number of contributors to PredictionIO has grown bigger and
> more diverse, we have sought for an independent framework for the project
> to keep thriving. We believe the Apache foundation is a great fit. Joining
> Apache would ensure that tried and true processes and procedures are in
> place for the growing number of organizations interested in contributing
> to PredictionIO. PredictionIO is also a good fit for the Apache foundation.
> PredictionIO was built on top of several Apache projects (HBase, Spark,
> Hadoop). We are familiar with the Apache process and believe that the
> democratic and meritocratic nature of the foundation aligns with the
> project goals.
>
> Initial Goals
>
> The initial milestones will be to move the existing codebase to Apache and
> integrate with the Apache development process. Once this is accomplished,
> we plan for incremental development and releases that follow the Apache
> guidelines, as well as growing our developer and user communities.
>
> Current Status
>
> PredictionIO has undergone nine minor releases and many patches.
> PredictionIO is being used in production by Salesforce.com as well as many
> other organizations and apps. The PredictionIO codebase is currently
> hosted at GitHub, which will form the basis of the Apache git repository.
>
> Meritocracy
>
> We plan to invest in supporting a meritocracy. We will discuss the
> requirements in an open forum. We intend to invite additional developers
> to participate. We will encourage and monitor community participation so
> that privileges can be extended to those that contribute.
>
> Community
>
> Acceptance into the Apache foundation would bolster the already strong
> user and developer community around PredictionIO. That community includes
> many contributors from various other companies, and an active mailing list
> composed of hundreds of users.
>
> Core Developers
>
> The core developers of our project are listed in our contributors and
> initial PPMC below. Though many are employed at Salesforce.com, there are
> also engineers from ActionML, and independent developers.
>
> Alignment
>
> The ASF is the natural choice to host the PredictionIO project as its goal
> is democratizing Machine Learning by making it more easily accessible to
> every user/developer. PredictionIO is built on top of several top level
> Apache projects as outlined above.
>
> Known Risks
>
> Orphaned Products
>
> PredictionIO has a solid and growing community. It is deployed on
> production environments by companies of all sizes to run various kinds of
> predictive engines.
>
> In addition to the community contribution to PredictionIO framework, the
> community is also actively contributing new engines to the Template
> Gallery as well as SDKs and documentation for the project. Salesforce is
> committed to utilize and advance the PredictionIO code base and support
> its user community.
>
> Inexperience with Open Source
>
> PredictionIO has existed as a healthy open source project for almost two
> years and is the most starred Scala project on GitHub. All of the proposed
> committers have contributed to ASF and Linux Foundation open source
> projects. Several current committers on Apache projects and Apache Members
> are involved in this proposal and intend to provide mentorship.
>
> Homogeneous Developers
>
> The initial list of committers includes developers from several
> institutions, including Salesforce, ActionML, Channel4, USC as well as
> unaffiliated developers.
>
> Reliance on Salaried Developers
>
> Like most open source projects, PredictionIO receives substantial support
> from salaried developers. PredictionIO development is partially supported
> by Salesforce.com, but there are many contributors from various other
> companies, and an active mailing list composed of hundreds of users. We
> will continue our efforts to ensure stewardship of the project to be
> independent of salaried developers by meritocratically promoting those
> contributors to committers.
>
> Relationships with Other Apache Product
>
> PredictionIO relies heavily on top level Apache projects such as Apache
> Spark, HBase and Hadoop. However it brings a distinguished functionality,
> rather than just an abstraction - Machine Learning in a plug-and-play
> fashion.
>
> Compared to Apache Mahout, which focuses on the development of a wide
> variety of algorithms, PredictionIO offers a platform to manage the whole
> machine learning workflow, including data collection, data preparation,
> modeling, deployment and management of predictive services in production
> environments.
>
> An Excessive Fascination with the Apache Brand
>
> PredictionIO is already a widely known open source project. This proposal
> is not for the purpose of generating publicity. Rather, the primary
> benefits to joining Apache are those outlined in the Rationale section.
>
> Documentation
>
> PredictionIO boasts rich and live documentation, included in the code repo
> (docs/manual directory), is built with Middleman, and publicly hosted at
> https://docs.prediction.io
>
> Initial Source and Intellectual Property Submission Plan
>
> Currently, the PredictionIO codebase is distributed under the Apache 2.0
> License and hosted on GitHub: https://github.com/PredictionIO/PredictionIO
>
> External Dependencies
>
> PredictionIO has the following external dependencies:
> * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are
> needed)
> * Apache Spark 1.3.0 for Hadoop 2.4
> * Java SE Development Kit 8
> * and one of the following sets:
> * PostgreSQL 9.1
> or
> * MySQL 5.1
> or
> * Apache HBase 0.98.6
> * Elasticsearch 1.4.0
>
> Upon acceptance to the incubator, we would begin a thorough analysis of
> all transitive dependencies to verify this information and introduce
> license checking into the build and release process by integrating with
> Apache RAT.
>
> Cryptography
>
> PredictionIO does not include cryptographic code. We utilize standard
> JCE and JSSE APIs provided by the Java Runtime Environment.
>
> Required Resources
>
> We request that following resources be created for the project to use
>
> Mailing lists
>
> predictionio-private@incubator.apache.org (with moderated subscriptions)
> predictionio-dev
> predictionio-user
> predictionio-commits
>
> We will migrate the existing PredictionIO mailing lists.
>
> Git repository
>
> The PredictionIO team would like to use Git for source control, due to
> our
> current use of GitHub.
>
> git://git.apache.org/incubator-predictionio
>
> Documentation
>
> https://predictionio.incubator.apache.org/docs/
>
> JIRA instance
>
> PredictionIO currently uses the GitHub issue tracking system associated
> with its repository: https://github.com/PredictionIO/PredictionIO/issues
> .
> We will migrate to Apache JIRA.
>
> JIRA PREDICTIONIO
> https://issues.apache.org/jira/browse/PREDICTIONIO
>
> Other Resources
>
> TravisCI for builds and test running.
>
> PredictionIO's documentation, included in the code repo (docs/manual
> directory), is built with Middleman and publicly hosted at
> https://docs.prediction.io
>
> A blog to drive adoption and excitement at https://blog.prediction.io
>
> Initial Committers
>
> Pat Ferrell
> Tamas Jambor
> Justin Yip
> Xusen Yin
> Lee Moon Soo
> Donald Szeto
> Kenneth Chan
> Tom Chan
> Simon Chan
> Marco Vivero
> Matthew Tovbin
> Yevgeny Khodorkovsky
> Felipe Oliveira
> Vitaly Gordon
> Alex Merritt
>
> Affiliations
>
> Pat Ferrell - ActionML
> Tamas Jambor - Channel4
> Justin Yip - independent
> Xusen Yin - USC
> Lee Moon Soo - NFLabs
> Donald Szeto - Salesforce
> Kenneth Chan - Salesforce
> Tom Chan - Salesforce
> Simon Chan - Salesforce
> Marco Vivero - Salesforce
> Matthew Tovbin - Salesforce
> Yevgeny Khodorkovsky - Salesforce
> Felipe Oliveira - Salesforce
> Vitaly Gordon - Salesforce
> Alex Merritt - ActionML
>
> Sponsors
>
> Champion
>
> Andrew Purtell <apurtell at apache dot org>
>
> Nominated Mentors
>
> Andrew Purtell <apurtell at apache dot org>
> James Taylor <jtaylor at apache dot org>
> Lars Hofhansl <larsh at apache dot org>
> Suneel Marthi <smarthi at apache dot org>
> Xiangrui Meng <meng at apache dot org>
> Luciano Resende <lresende at apache dot org>
>
> Sponsoring Entity
>
> Apache Incubator PMC
>
>
> --
> Best regards,
>
> - Andy
>
> Problems worthy of attack prove their worth by hitting back. - Piet Hein
> (via Tom White)
>
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Jean-Baptiste Onofré <jb...@nanthrax.net>.
+1 (binding)
Regards
JB
On 05/24/2016 12:22 AM, Andrew Purtell wrote:
> Since discussion on the matter of PredictionIO has died down, I would like
> to call a VOTE
> on accepting PredictionIO into the Apache Incubator.
>
> Proposal: https://wiki.apache.org/incubator/PredictionIO
>
> \u200b[ ] +1 Accept PredictionIO into the Apache Incubator
> [ ] +0 Abstain
> [ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
>
> This vote will be open for at least 72 hours.
>
> My vote is +1 (binding)
>
> --
>
> PredictionIO Proposal
>
> Abstract
>
> PredictionIO is an open source Machine Learning Server built on top of
> state-of-the-art open source stack, that enables developers to manage and
> deploy production-ready predictive services for various kinds of machine
> learning tasks.
>
> Proposal
>
> The PredictionIO platform consists of the following components:
>
> * PredictionIO framework - provides the machine learning stack for
> building, evaluating and deploying engines with machine learning
> algorithms. It uses Apache Spark for processing.
>
> * Event Server - the machine learning analytics layer for unifying events
> from multiple platforms. It can use Apache HBase or any JDBC backends
> as its data store.
>
> The PredictionIO community also maintains a Template Gallery, a place to
> publish and download (free or proprietary) engine templates for different
> types of machine learning applications, and is a complemental part of the
> project. At this point we exclude the Template Gallery from the proposal,
> as it has a separate set of contributors and we\u2019re not familiar with an
> Apache approved mechanism to maintain such a gallery.
>
> Background
>
> PredictionIO was started with a mission to democratize and bring machine
> learning to the masses.
>
> Machine learning has traditionally been a luxury for big companies like
> Google, Facebook, and Netflix. There are ML libraries and tools lying
> around the internet but the effort of putting them all together as a
> production-ready infrastructure is a very resource-intensive task that is
> remotely reachable by individuals or small businesses.
>
> PredictionIO is a production-ready, full stack machine learning system that
> allows organizations of any scale to quickly deploy machine learning
> capabilities. It comes with official and community-contributed machine
> learning engine templates that are easy to customize.
>
> Rationale
>
> As usage and number of contributors to PredictionIO has grown bigger and
> more diverse, we have sought for an independent framework for the project
> to keep thriving. We believe the Apache foundation is a great fit. Joining
> Apache would ensure that tried and true processes and procedures are in
> place for the growing number of organizations interested in contributing
> to PredictionIO. PredictionIO is also a good fit for the Apache foundation.
> PredictionIO was built on top of several Apache projects (HBase, Spark,
> Hadoop). We are familiar with the Apache process and believe that the
> democratic and meritocratic nature of the foundation aligns with the
> project goals.
>
> Initial Goals
>
> The initial milestones will be to move the existing codebase to Apache and
> integrate with the Apache development process. Once this is accomplished,
> we plan for incremental development and releases that follow the Apache
> guidelines, as well as growing our developer and user communities.
>
> Current Status
>
> PredictionIO has undergone nine minor releases and many patches.
> PredictionIO is being used in production by Salesforce.com as well as many
> other organizations and apps. The PredictionIO codebase is currently
> hosted at GitHub, which will form the basis of the Apache git repository.
>
> Meritocracy
>
> We plan to invest in supporting a meritocracy. We will discuss the
> requirements in an open forum. We intend to invite additional developers
> to participate. We will encourage and monitor community participation so
> that privileges can be extended to those that contribute.
>
> Community
>
> Acceptance into the Apache foundation would bolster the already strong
> user and developer community around PredictionIO. That community includes
> many contributors from various other companies, and an active mailing list
> composed of hundreds of users.
>
> Core Developers
>
> The core developers of our project are listed in our contributors and
> initial PPMC below. Though many are employed at Salesforce.com, there are
> also engineers from ActionML, and independent developers.
>
> Alignment
>
> The ASF is the natural choice to host the PredictionIO project as its goal
> is democratizing Machine Learning by making it more easily accessible to
> every user/developer. PredictionIO is built on top of several top level
> Apache projects as outlined above.
>
> Known Risks
>
> Orphaned Products
>
> PredictionIO has a solid and growing community. It is deployed on
> production environments by companies of all sizes to run various kinds of
> predictive engines.
>
> In addition to the community contribution to PredictionIO framework, the
> community is also actively contributing new engines to the Template
> Gallery as well as SDKs and documentation for the project. Salesforce is
> committed to utilize and advance the PredictionIO code base and support
> its user community.
>
> Inexperience with Open Source
>
> PredictionIO has existed as a healthy open source project for almost two
> years and is the most starred Scala project on GitHub. All of the proposed
> committers have contributed to ASF and Linux Foundation open source
> projects. Several current committers on Apache projects and Apache Members
> are involved in this proposal and intend to provide mentorship.
>
> Homogeneous Developers
>
> The initial list of committers includes developers from several
> institutions, including Salesforce, ActionML, Channel4, USC as well as
> unaffiliated developers.
>
> Reliance on Salaried Developers
>
> Like most open source projects, PredictionIO receives substantial support
> from salaried developers. PredictionIO development is partially supported
> by Salesforce.com, but there are many contributors from various other
> companies, and an active mailing list composed of hundreds of users. We
> will continue our efforts to ensure stewardship of the project to be
> independent of salaried developers by meritocratically promoting those
> contributors to committers.
>
> Relationships with Other Apache Product
>
> PredictionIO relies heavily on top level Apache projects such as Apache
> Spark, HBase and Hadoop. However it brings a distinguished functionality,
> rather than just an abstraction - Machine Learning in a plug-and-play
> fashion.
>
> Compared to Apache Mahout, which focuses on the development of a wide
> variety of algorithms, PredictionIO offers a platform to manage the whole
> machine learning workflow, including data collection, data preparation,
> modeling, deployment and management of predictive services in production
> environments.
>
> An Excessive Fascination with the Apache Brand
>
> PredictionIO is already a widely known open source project. This proposal
> is not for the purpose of generating publicity. Rather, the primary
> benefits to joining Apache are those outlined in the Rationale section.
>
> Documentation
>
> PredictionIO boasts rich and live documentation, included in the code repo
> (docs/manual directory), is built with Middleman, and publicly hosted at
> https://docs.prediction.io
>
> Initial Source and Intellectual Property Submission Plan
>
> Currently, the PredictionIO codebase is distributed under the Apache 2.0
> License and hosted on GitHub: https://github.com/PredictionIO/PredictionIO
>
> External Dependencies
>
> PredictionIO has the following external dependencies:
> * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are needed)
> * Apache Spark 1.3.0 for Hadoop 2.4
> * Java SE Development Kit 8
> * and one of the following sets:
> * PostgreSQL 9.1
> or
> * MySQL 5.1
> or
> * Apache HBase 0.98.6
> * Elasticsearch 1.4.0
>
> Upon acceptance to the incubator, we would begin a thorough analysis of
> all transitive dependencies to verify this information and introduce
> license checking into the build and release process by integrating with
> Apache RAT.
>
> Cryptography
>
> PredictionIO does not include cryptographic code. We utilize standard
> JCE and JSSE APIs provided by the Java Runtime Environment.
>
> Required Resources
>
> We request that following resources be created for the project to use
>
> Mailing lists
>
> predictionio-private@incubator.apache.org (with moderated subscriptions)
> predictionio-dev
> predictionio-user
> predictionio-commits
>
> We will migrate the existing PredictionIO mailing lists.
>
> Git repository
>
> The PredictionIO team would like to use Git for source control, due to our
> current use of GitHub.
>
> git://git.apache.org/incubator-predictionio
>
> Documentation
>
> https://predictionio.incubator.apache.org/docs/
>
> JIRA instance
>
> PredictionIO currently uses the GitHub issue tracking system associated
> with its repository: https://github.com/PredictionIO/PredictionIO/issues.
> We will migrate to Apache JIRA.
>
> JIRA PREDICTIONIO
> https://issues.apache.org/jira/browse/PREDICTIONIO
>
> Other Resources
>
> TravisCI for builds and test running.
>
> PredictionIO's documentation, included in the code repo (docs/manual
> directory), is built with Middleman and publicly hosted at
> https://docs.prediction.io
>
> A blog to drive adoption and excitement at https://blog.prediction.io
>
> Initial Committers
>
> Pat Ferrell
> Tamas Jambor
> Justin Yip
> Xusen Yin
> Lee Moon Soo
> Donald Szeto
> Kenneth Chan
> Tom Chan
> Simon Chan
> Marco Vivero
> Matthew Tovbin
> Yevgeny Khodorkovsky
> Felipe Oliveira
> Vitaly Gordon
> Alex Merritt
>
> Affiliations
>
> Pat Ferrell - ActionML
> Tamas Jambor - Channel4
> Justin Yip - independent
> Xusen Yin - USC
> Lee Moon Soo - NFLabs
> Donald Szeto - Salesforce
> Kenneth Chan - Salesforce
> Tom Chan - Salesforce
> Simon Chan - Salesforce
> Marco Vivero - Salesforce
> Matthew Tovbin - Salesforce
> Yevgeny Khodorkovsky - Salesforce
> Felipe Oliveira - Salesforce
> Vitaly Gordon - Salesforce
> Alex Merritt - ActionML
>
> Sponsors
>
> Champion
>
> Andrew Purtell <apurtell at apache dot org>
>
> Nominated Mentors
>
> Andrew Purtell <apurtell at apache dot org>
> James Taylor <jtaylor at apache dot org>
> Lars Hofhansl <larsh at apache dot org>
> Suneel Marthi <smarthi at apache dot org>
> Xiangrui Meng <meng at apache dot org>
> Luciano Resende <lresende at apache dot org>
>
> Sponsoring Entity
>
> Apache Incubator PMC
>
>
--
Jean-Baptiste Onofr�
jbonofre@apache.org
http://blog.nanthrax.net
Talend - http://www.talend.com
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Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Alexander Bezzubov <bz...@apache.org>.
+1 (non-binding) great to see it coming to ASF
On Tue, May 24, 2016 at 2:20 PM, moon soo Lee <mo...@apache.org> wrote:
> +1 (non-binding)
>
> On Mon, May 23, 2016 at 3:23 PM Andrew Purtell <ap...@apache.org>
> wrote:
>
> > Since discussion on the matter of PredictionIO has died down, I would
> like
> > to call a VOTE
> > on accepting PredictionIO into the Apache Incubator.
> >
> > Proposal: https://wiki.apache.org/incubator/PredictionIO
> >
> > [ ] +1 Accept PredictionIO into the Apache Incubator
> > [ ] +0 Abstain
> > [ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
> >
> > This vote will be open for at least 72 hours.
> >
> > My vote is +1 (binding)
> >
> > --
> >
>
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by moon soo Lee <mo...@apache.org>.
+1 (non-binding)
On Mon, May 23, 2016 at 3:23 PM Andrew Purtell <ap...@apache.org> wrote:
> Since discussion on the matter of PredictionIO has died down, I would like
> to call a VOTE
> on accepting PredictionIO into the Apache Incubator.
>
> Proposal: https://wiki.apache.org/incubator/PredictionIO
>
> [ ] +1 Accept PredictionIO into the Apache Incubator
> [ ] +0 Abstain
> [ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
>
> This vote will be open for at least 72 hours.
>
> My vote is +1 (binding)
>
> --
>
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Roman Shaposhnik <ro...@shaposhnik.org>.
On Mon, May 23, 2016 at 3:22 PM, Andrew Purtell <ap...@apache.org> wrote:
> Since discussion on the matter of PredictionIO has died down, I would like
> to call a VOTE
> on accepting PredictionIO into the Apache Incubator.
>
> Proposal: https://wiki.apache.org/incubator/PredictionIO
>
> [ ] +1 Accept PredictionIO into the Apache Incubator
> [ ] +0 Abstain
> [ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
+1 (binding)
Thanks,
Roman.
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Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Felix Cheung <fe...@apache.org>.
+1 (non-binding)
On Mon, May 23, 2016 at 5:46 PM Henry Saputra <he...@gmail.com>
wrote:
> +1 (binding)
>
> On Mon, May 23, 2016 at 4:46 PM, Ted Dunning <te...@gmail.com>
> wrote:
>
> > +1 (binding)
> >
> >
> >
> > On Mon, May 23, 2016 at 7:31 PM, Debo Dutta (dedutta) <dedutta@cisco.com
> >
> > wrote:
> >
> > > +1
> > >
> > >
> > >
> > >
> > > On 5/23/16, 3:22 PM, "Andrew Purtell" <ap...@apache.org> wrote:
> > >
> > > >Since discussion on the matter of PredictionIO has died down, I would
> > like
> > > >to call a VOTE
> > > >on accepting PredictionIO into the Apache Incubator.
> > > >
> > > >Proposal: https://wiki.apache.org/incubator/PredictionIO
> > > >
> > > >[ ] +1 Accept PredictionIO into the Apache Incubator
> > > >[ ] +0 Abstain
> > > >[ ] -1 Do not accept PredictionIO into the Apache Incubator, because
> ...
> > > >
> > > >This vote will be open for at least 72 hours.
> > > >
> > > >My vote is +1 (binding)
> > > >
> > > >--
> > > >
> > > >PredictionIO Proposal
> > > >
> > > >Abstract
> > > >
> > > >PredictionIO is an open source Machine Learning Server built on top of
> > > >state-of-the-art open source stack, that enables developers to manage
> > and
> > > >deploy production-ready predictive services for various kinds of
> machine
> > > >learning tasks.
> > > >
> > > >Proposal
> > > >
> > > >The PredictionIO platform consists of the following components:
> > > >
> > > > * PredictionIO framework - provides the machine learning stack for
> > > > building, evaluating and deploying engines with machine learning
> > > > algorithms. It uses Apache Spark for processing.
> > > >
> > > > * Event Server - the machine learning analytics layer for unifying
> > > events
> > > > from multiple platforms. It can use Apache HBase or any JDBC
> > backends
> > > > as its data store.
> > > >
> > > >The PredictionIO community also maintains a Template Gallery, a place
> to
> > > >publish and download (free or proprietary) engine templates for
> > different
> > > >types of machine learning applications, and is a complemental part of
> > the
> > > >project. At this point we exclude the Template Gallery from the
> > proposal,
> > > >as it has a separate set of contributors and we’re not familiar with
> an
> > > >Apache approved mechanism to maintain such a gallery.
> > > >
> > > >Background
> > > >
> > > >PredictionIO was started with a mission to democratize and bring
> machine
> > > >learning to the masses.
> > > >
> > > >Machine learning has traditionally been a luxury for big companies
> like
> > > >Google, Facebook, and Netflix. There are ML libraries and tools lying
> > > >around the internet but the effort of putting them all together as a
> > > >production-ready infrastructure is a very resource-intensive task that
> > is
> > > >remotely reachable by individuals or small businesses.
> > > >
> > > >PredictionIO is a production-ready, full stack machine learning system
> > > that
> > > >allows organizations of any scale to quickly deploy machine learning
> > > >capabilities. It comes with official and community-contributed machine
> > > >learning engine templates that are easy to customize.
> > > >
> > > >Rationale
> > > >
> > > >As usage and number of contributors to PredictionIO has grown bigger
> and
> > > >more diverse, we have sought for an independent framework for the
> > project
> > > >to keep thriving. We believe the Apache foundation is a great fit.
> > Joining
> > > >Apache would ensure that tried and true processes and procedures are
> in
> > > >place for the growing number of organizations interested in
> contributing
> > > >to PredictionIO. PredictionIO is also a good fit for the Apache
> > > foundation.
> > > >PredictionIO was built on top of several Apache projects (HBase,
> Spark,
> > > >Hadoop). We are familiar with the Apache process and believe that the
> > > >democratic and meritocratic nature of the foundation aligns with the
> > > >project goals.
> > > >
> > > >Initial Goals
> > > >
> > > >The initial milestones will be to move the existing codebase to Apache
> > and
> > > >integrate with the Apache development process. Once this is
> > accomplished,
> > > >we plan for incremental development and releases that follow the
> Apache
> > > >guidelines, as well as growing our developer and user communities.
> > > >
> > > >Current Status
> > > >
> > > >PredictionIO has undergone nine minor releases and many patches.
> > > >PredictionIO is being used in production by Salesforce.com as well as
> > many
> > > >other organizations and apps. The PredictionIO codebase is currently
> > > >hosted at GitHub, which will form the basis of the Apache git
> > repository.
> > > >
> > > >Meritocracy
> > > >
> > > >We plan to invest in supporting a meritocracy. We will discuss the
> > > >requirements in an open forum. We intend to invite additional
> developers
> > > >to participate. We will encourage and monitor community participation
> so
> > > >that privileges can be extended to those that contribute.
> > > >
> > > >Community
> > > >
> > > >Acceptance into the Apache foundation would bolster the already strong
> > > >user and developer community around PredictionIO. That community
> > includes
> > > >many contributors from various other companies, and an active mailing
> > list
> > > >composed of hundreds of users.
> > > >
> > > >Core Developers
> > > >
> > > >The core developers of our project are listed in our contributors and
> > > >initial PPMC below. Though many are employed at Salesforce.com, there
> > are
> > > >also engineers from ActionML, and independent developers.
> > > >
> > > >Alignment
> > > >
> > > >The ASF is the natural choice to host the PredictionIO project as its
> > goal
> > > >is democratizing Machine Learning by making it more easily accessible
> to
> > > >every user/developer. PredictionIO is built on top of several top
> level
> > > >Apache projects as outlined above.
> > > >
> > > >Known Risks
> > > >
> > > >Orphaned Products
> > > >
> > > >PredictionIO has a solid and growing community. It is deployed on
> > > >production environments by companies of all sizes to run various kinds
> > of
> > > >predictive engines.
> > > >
> > > >In addition to the community contribution to PredictionIO framework,
> the
> > > >community is also actively contributing new engines to the Template
> > > >Gallery as well as SDKs and documentation for the project. Salesforce
> is
> > > >committed to utilize and advance the PredictionIO code base and
> support
> > > >its user community.
> > > >
> > > >Inexperience with Open Source
> > > >
> > > >PredictionIO has existed as a healthy open source project for almost
> two
> > > >years and is the most starred Scala project on GitHub. All of the
> > proposed
> > > >committers have contributed to ASF and Linux Foundation open source
> > > >projects. Several current committers on Apache projects and Apache
> > Members
> > > >are involved in this proposal and intend to provide mentorship.
> > > >
> > > >Homogeneous Developers
> > > >
> > > >The initial list of committers includes developers from several
> > > >institutions, including Salesforce, ActionML, Channel4, USC as well as
> > > >unaffiliated developers.
> > > >
> > > >Reliance on Salaried Developers
> > > >
> > > >Like most open source projects, PredictionIO receives substantial
> > support
> > > >from salaried developers. PredictionIO development is partially
> > supported
> > > >by Salesforce.com, but there are many contributors from various other
> > > >companies, and an active mailing list composed of hundreds of users.
> We
> > > >will continue our efforts to ensure stewardship of the project to be
> > > >independent of salaried developers by meritocratically promoting those
> > > >contributors to committers.
> > > >
> > > >Relationships with Other Apache Product
> > > >
> > > >PredictionIO relies heavily on top level Apache projects such as
> Apache
> > > >Spark, HBase and Hadoop. However it brings a distinguished
> > functionality,
> > > >rather than just an abstraction - Machine Learning in a plug-and-play
> > > >fashion.
> > > >
> > > >Compared to Apache Mahout, which focuses on the development of a wide
> > > >variety of algorithms, PredictionIO offers a platform to manage the
> > whole
> > > >machine learning workflow, including data collection, data
> preparation,
> > > >modeling, deployment and management of predictive services in
> production
> > > >environments.
> > > >
> > > >An Excessive Fascination with the Apache Brand
> > > >
> > > >PredictionIO is already a widely known open source project. This
> > proposal
> > > >is not for the purpose of generating publicity. Rather, the primary
> > > >benefits to joining Apache are those outlined in the Rationale
> section.
> > > >
> > > >Documentation
> > > >
> > > >PredictionIO boasts rich and live documentation, included in the code
> > repo
> > > >(docs/manual directory), is built with Middleman, and publicly hosted
> at
> > > >https://docs.prediction.io
> > > >
> > > >Initial Source and Intellectual Property Submission Plan
> > > >
> > > >Currently, the PredictionIO codebase is distributed under the Apache
> 2.0
> > > >License and hosted on GitHub:
> > > https://github.com/PredictionIO/PredictionIO
> > > >
> > > >External Dependencies
> > > >
> > > >PredictionIO has the following external dependencies:
> > > > * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are
> > > needed)
> > > > * Apache Spark 1.3.0 for Hadoop 2.4
> > > > * Java SE Development Kit 8
> > > > * and one of the following sets:
> > > > * PostgreSQL 9.1
> > > > or
> > > > * MySQL 5.1
> > > > or
> > > > * Apache HBase 0.98.6
> > > > * Elasticsearch 1.4.0
> > > >
> > > >Upon acceptance to the incubator, we would begin a thorough analysis
> of
> > > >all transitive dependencies to verify this information and introduce
> > > >license checking into the build and release process by integrating
> with
> > > >Apache RAT.
> > > >
> > > >Cryptography
> > > >
> > > >PredictionIO does not include cryptographic code. We utilize standard
> > > >JCE and JSSE APIs provided by the Java Runtime Environment.
> > > >
> > > >Required Resources
> > > >
> > > >We request that following resources be created for the project to use
> > > >
> > > >Mailing lists
> > > >
> > > > predictionio-private@incubator.apache.org (with moderated
> > > subscriptions)
> > > > predictionio-dev
> > > > predictionio-user
> > > > predictionio-commits
> > > >
> > > > We will migrate the existing PredictionIO mailing lists.
> > > >
> > > >Git repository
> > > >
> > > > The PredictionIO team would like to use Git for source control, due
> to
> > > our
> > > > current use of GitHub.
> > > >
> > > > git://git.apache.org/incubator-predictionio
> > > >
> > > >Documentation
> > > >
> > > > https://predictionio.incubator.apache.org/docs/
> > > >
> > > >JIRA instance
> > > >
> > > > PredictionIO currently uses the GitHub issue tracking system
> > associated
> > > > with its repository:
> > > https://github.com/PredictionIO/PredictionIO/issues.
> > > > We will migrate to Apache JIRA.
> > > >
> > > > JIRA PREDICTIONIO
> > > > https://issues.apache.org/jira/browse/PREDICTIONIO
> > > >
> > > >Other Resources
> > > >
> > > > TravisCI for builds and test running.
> > > >
> > > > PredictionIO's documentation, included in the code repo (docs/manual
> > > > directory), is built with Middleman and publicly hosted at
> > > > https://docs.prediction.io
> > > >
> > > > A blog to drive adoption and excitement at
> https://blog.prediction.io
> > > >
> > > >Initial Committers
> > > >
> > > > Pat Ferrell
> > > > Tamas Jambor
> > > > Justin Yip
> > > > Xusen Yin
> > > > Lee Moon Soo
> > > > Donald Szeto
> > > > Kenneth Chan
> > > > Tom Chan
> > > > Simon Chan
> > > > Marco Vivero
> > > > Matthew Tovbin
> > > > Yevgeny Khodorkovsky
> > > > Felipe Oliveira
> > > > Vitaly Gordon
> > > > Alex Merritt
> > > >
> > > >Affiliations
> > > >
> > > > Pat Ferrell - ActionML
> > > > Tamas Jambor - Channel4
> > > > Justin Yip - independent
> > > > Xusen Yin - USC
> > > > Lee Moon Soo - NFLabs
> > > > Donald Szeto - Salesforce
> > > > Kenneth Chan - Salesforce
> > > > Tom Chan - Salesforce
> > > > Simon Chan - Salesforce
> > > > Marco Vivero - Salesforce
> > > > Matthew Tovbin - Salesforce
> > > > Yevgeny Khodorkovsky - Salesforce
> > > > Felipe Oliveira - Salesforce
> > > > Vitaly Gordon - Salesforce
> > > > Alex Merritt - ActionML
> > > >
> > > >Sponsors
> > > >
> > > >Champion
> > > >
> > > > Andrew Purtell <apurtell at apache dot org>
> > > >
> > > >Nominated Mentors
> > > >
> > > > Andrew Purtell <apurtell at apache dot org>
> > > > James Taylor <jtaylor at apache dot org>
> > > > Lars Hofhansl <larsh at apache dot org>
> > > > Suneel Marthi <smarthi at apache dot org>
> > > > Xiangrui Meng <meng at apache dot org>
> > > > Luciano Resende <lresende at apache dot org>
> > > >
> > > >Sponsoring Entity
> > > >
> > > > Apache Incubator PMC
> > > >
> > > >
> > > >--
> > > >Best regards,
> > > >
> > > > - Andy
> > > >
> > > >Problems worthy of attack prove their worth by hitting back. - Piet
> Hein
> > > >(via Tom White)
> > >
> >
>
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Henry Saputra <he...@gmail.com>.
+1 (binding)
On Mon, May 23, 2016 at 4:46 PM, Ted Dunning <te...@gmail.com> wrote:
> +1 (binding)
>
>
>
> On Mon, May 23, 2016 at 7:31 PM, Debo Dutta (dedutta) <de...@cisco.com>
> wrote:
>
> > +1
> >
> >
> >
> >
> > On 5/23/16, 3:22 PM, "Andrew Purtell" <ap...@apache.org> wrote:
> >
> > >Since discussion on the matter of PredictionIO has died down, I would
> like
> > >to call a VOTE
> > >on accepting PredictionIO into the Apache Incubator.
> > >
> > >Proposal: https://wiki.apache.org/incubator/PredictionIO
> > >
> > >[ ] +1 Accept PredictionIO into the Apache Incubator
> > >[ ] +0 Abstain
> > >[ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
> > >
> > >This vote will be open for at least 72 hours.
> > >
> > >My vote is +1 (binding)
> > >
> > >--
> > >
> > >PredictionIO Proposal
> > >
> > >Abstract
> > >
> > >PredictionIO is an open source Machine Learning Server built on top of
> > >state-of-the-art open source stack, that enables developers to manage
> and
> > >deploy production-ready predictive services for various kinds of machine
> > >learning tasks.
> > >
> > >Proposal
> > >
> > >The PredictionIO platform consists of the following components:
> > >
> > > * PredictionIO framework - provides the machine learning stack for
> > > building, evaluating and deploying engines with machine learning
> > > algorithms. It uses Apache Spark for processing.
> > >
> > > * Event Server - the machine learning analytics layer for unifying
> > events
> > > from multiple platforms. It can use Apache HBase or any JDBC
> backends
> > > as its data store.
> > >
> > >The PredictionIO community also maintains a Template Gallery, a place to
> > >publish and download (free or proprietary) engine templates for
> different
> > >types of machine learning applications, and is a complemental part of
> the
> > >project. At this point we exclude the Template Gallery from the
> proposal,
> > >as it has a separate set of contributors and we’re not familiar with an
> > >Apache approved mechanism to maintain such a gallery.
> > >
> > >Background
> > >
> > >PredictionIO was started with a mission to democratize and bring machine
> > >learning to the masses.
> > >
> > >Machine learning has traditionally been a luxury for big companies like
> > >Google, Facebook, and Netflix. There are ML libraries and tools lying
> > >around the internet but the effort of putting them all together as a
> > >production-ready infrastructure is a very resource-intensive task that
> is
> > >remotely reachable by individuals or small businesses.
> > >
> > >PredictionIO is a production-ready, full stack machine learning system
> > that
> > >allows organizations of any scale to quickly deploy machine learning
> > >capabilities. It comes with official and community-contributed machine
> > >learning engine templates that are easy to customize.
> > >
> > >Rationale
> > >
> > >As usage and number of contributors to PredictionIO has grown bigger and
> > >more diverse, we have sought for an independent framework for the
> project
> > >to keep thriving. We believe the Apache foundation is a great fit.
> Joining
> > >Apache would ensure that tried and true processes and procedures are in
> > >place for the growing number of organizations interested in contributing
> > >to PredictionIO. PredictionIO is also a good fit for the Apache
> > foundation.
> > >PredictionIO was built on top of several Apache projects (HBase, Spark,
> > >Hadoop). We are familiar with the Apache process and believe that the
> > >democratic and meritocratic nature of the foundation aligns with the
> > >project goals.
> > >
> > >Initial Goals
> > >
> > >The initial milestones will be to move the existing codebase to Apache
> and
> > >integrate with the Apache development process. Once this is
> accomplished,
> > >we plan for incremental development and releases that follow the Apache
> > >guidelines, as well as growing our developer and user communities.
> > >
> > >Current Status
> > >
> > >PredictionIO has undergone nine minor releases and many patches.
> > >PredictionIO is being used in production by Salesforce.com as well as
> many
> > >other organizations and apps. The PredictionIO codebase is currently
> > >hosted at GitHub, which will form the basis of the Apache git
> repository.
> > >
> > >Meritocracy
> > >
> > >We plan to invest in supporting a meritocracy. We will discuss the
> > >requirements in an open forum. We intend to invite additional developers
> > >to participate. We will encourage and monitor community participation so
> > >that privileges can be extended to those that contribute.
> > >
> > >Community
> > >
> > >Acceptance into the Apache foundation would bolster the already strong
> > >user and developer community around PredictionIO. That community
> includes
> > >many contributors from various other companies, and an active mailing
> list
> > >composed of hundreds of users.
> > >
> > >Core Developers
> > >
> > >The core developers of our project are listed in our contributors and
> > >initial PPMC below. Though many are employed at Salesforce.com, there
> are
> > >also engineers from ActionML, and independent developers.
> > >
> > >Alignment
> > >
> > >The ASF is the natural choice to host the PredictionIO project as its
> goal
> > >is democratizing Machine Learning by making it more easily accessible to
> > >every user/developer. PredictionIO is built on top of several top level
> > >Apache projects as outlined above.
> > >
> > >Known Risks
> > >
> > >Orphaned Products
> > >
> > >PredictionIO has a solid and growing community. It is deployed on
> > >production environments by companies of all sizes to run various kinds
> of
> > >predictive engines.
> > >
> > >In addition to the community contribution to PredictionIO framework, the
> > >community is also actively contributing new engines to the Template
> > >Gallery as well as SDKs and documentation for the project. Salesforce is
> > >committed to utilize and advance the PredictionIO code base and support
> > >its user community.
> > >
> > >Inexperience with Open Source
> > >
> > >PredictionIO has existed as a healthy open source project for almost two
> > >years and is the most starred Scala project on GitHub. All of the
> proposed
> > >committers have contributed to ASF and Linux Foundation open source
> > >projects. Several current committers on Apache projects and Apache
> Members
> > >are involved in this proposal and intend to provide mentorship.
> > >
> > >Homogeneous Developers
> > >
> > >The initial list of committers includes developers from several
> > >institutions, including Salesforce, ActionML, Channel4, USC as well as
> > >unaffiliated developers.
> > >
> > >Reliance on Salaried Developers
> > >
> > >Like most open source projects, PredictionIO receives substantial
> support
> > >from salaried developers. PredictionIO development is partially
> supported
> > >by Salesforce.com, but there are many contributors from various other
> > >companies, and an active mailing list composed of hundreds of users. We
> > >will continue our efforts to ensure stewardship of the project to be
> > >independent of salaried developers by meritocratically promoting those
> > >contributors to committers.
> > >
> > >Relationships with Other Apache Product
> > >
> > >PredictionIO relies heavily on top level Apache projects such as Apache
> > >Spark, HBase and Hadoop. However it brings a distinguished
> functionality,
> > >rather than just an abstraction - Machine Learning in a plug-and-play
> > >fashion.
> > >
> > >Compared to Apache Mahout, which focuses on the development of a wide
> > >variety of algorithms, PredictionIO offers a platform to manage the
> whole
> > >machine learning workflow, including data collection, data preparation,
> > >modeling, deployment and management of predictive services in production
> > >environments.
> > >
> > >An Excessive Fascination with the Apache Brand
> > >
> > >PredictionIO is already a widely known open source project. This
> proposal
> > >is not for the purpose of generating publicity. Rather, the primary
> > >benefits to joining Apache are those outlined in the Rationale section.
> > >
> > >Documentation
> > >
> > >PredictionIO boasts rich and live documentation, included in the code
> repo
> > >(docs/manual directory), is built with Middleman, and publicly hosted at
> > >https://docs.prediction.io
> > >
> > >Initial Source and Intellectual Property Submission Plan
> > >
> > >Currently, the PredictionIO codebase is distributed under the Apache 2.0
> > >License and hosted on GitHub:
> > https://github.com/PredictionIO/PredictionIO
> > >
> > >External Dependencies
> > >
> > >PredictionIO has the following external dependencies:
> > > * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are
> > needed)
> > > * Apache Spark 1.3.0 for Hadoop 2.4
> > > * Java SE Development Kit 8
> > > * and one of the following sets:
> > > * PostgreSQL 9.1
> > > or
> > > * MySQL 5.1
> > > or
> > > * Apache HBase 0.98.6
> > > * Elasticsearch 1.4.0
> > >
> > >Upon acceptance to the incubator, we would begin a thorough analysis of
> > >all transitive dependencies to verify this information and introduce
> > >license checking into the build and release process by integrating with
> > >Apache RAT.
> > >
> > >Cryptography
> > >
> > >PredictionIO does not include cryptographic code. We utilize standard
> > >JCE and JSSE APIs provided by the Java Runtime Environment.
> > >
> > >Required Resources
> > >
> > >We request that following resources be created for the project to use
> > >
> > >Mailing lists
> > >
> > > predictionio-private@incubator.apache.org (with moderated
> > subscriptions)
> > > predictionio-dev
> > > predictionio-user
> > > predictionio-commits
> > >
> > > We will migrate the existing PredictionIO mailing lists.
> > >
> > >Git repository
> > >
> > > The PredictionIO team would like to use Git for source control, due to
> > our
> > > current use of GitHub.
> > >
> > > git://git.apache.org/incubator-predictionio
> > >
> > >Documentation
> > >
> > > https://predictionio.incubator.apache.org/docs/
> > >
> > >JIRA instance
> > >
> > > PredictionIO currently uses the GitHub issue tracking system
> associated
> > > with its repository:
> > https://github.com/PredictionIO/PredictionIO/issues.
> > > We will migrate to Apache JIRA.
> > >
> > > JIRA PREDICTIONIO
> > > https://issues.apache.org/jira/browse/PREDICTIONIO
> > >
> > >Other Resources
> > >
> > > TravisCI for builds and test running.
> > >
> > > PredictionIO's documentation, included in the code repo (docs/manual
> > > directory), is built with Middleman and publicly hosted at
> > > https://docs.prediction.io
> > >
> > > A blog to drive adoption and excitement at https://blog.prediction.io
> > >
> > >Initial Committers
> > >
> > > Pat Ferrell
> > > Tamas Jambor
> > > Justin Yip
> > > Xusen Yin
> > > Lee Moon Soo
> > > Donald Szeto
> > > Kenneth Chan
> > > Tom Chan
> > > Simon Chan
> > > Marco Vivero
> > > Matthew Tovbin
> > > Yevgeny Khodorkovsky
> > > Felipe Oliveira
> > > Vitaly Gordon
> > > Alex Merritt
> > >
> > >Affiliations
> > >
> > > Pat Ferrell - ActionML
> > > Tamas Jambor - Channel4
> > > Justin Yip - independent
> > > Xusen Yin - USC
> > > Lee Moon Soo - NFLabs
> > > Donald Szeto - Salesforce
> > > Kenneth Chan - Salesforce
> > > Tom Chan - Salesforce
> > > Simon Chan - Salesforce
> > > Marco Vivero - Salesforce
> > > Matthew Tovbin - Salesforce
> > > Yevgeny Khodorkovsky - Salesforce
> > > Felipe Oliveira - Salesforce
> > > Vitaly Gordon - Salesforce
> > > Alex Merritt - ActionML
> > >
> > >Sponsors
> > >
> > >Champion
> > >
> > > Andrew Purtell <apurtell at apache dot org>
> > >
> > >Nominated Mentors
> > >
> > > Andrew Purtell <apurtell at apache dot org>
> > > James Taylor <jtaylor at apache dot org>
> > > Lars Hofhansl <larsh at apache dot org>
> > > Suneel Marthi <smarthi at apache dot org>
> > > Xiangrui Meng <meng at apache dot org>
> > > Luciano Resende <lresende at apache dot org>
> > >
> > >Sponsoring Entity
> > >
> > > Apache Incubator PMC
> > >
> > >
> > >--
> > >Best regards,
> > >
> > > - Andy
> > >
> > >Problems worthy of attack prove their worth by hitting back. - Piet Hein
> > >(via Tom White)
> >
>
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by Ted Dunning <te...@gmail.com>.
+1 (binding)
On Mon, May 23, 2016 at 7:31 PM, Debo Dutta (dedutta) <de...@cisco.com>
wrote:
> +1
>
>
>
>
> On 5/23/16, 3:22 PM, "Andrew Purtell" <ap...@apache.org> wrote:
>
> >Since discussion on the matter of PredictionIO has died down, I would like
> >to call a VOTE
> >on accepting PredictionIO into the Apache Incubator.
> >
> >Proposal: https://wiki.apache.org/incubator/PredictionIO
> >
> >[ ] +1 Accept PredictionIO into the Apache Incubator
> >[ ] +0 Abstain
> >[ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
> >
> >This vote will be open for at least 72 hours.
> >
> >My vote is +1 (binding)
> >
> >--
> >
> >PredictionIO Proposal
> >
> >Abstract
> >
> >PredictionIO is an open source Machine Learning Server built on top of
> >state-of-the-art open source stack, that enables developers to manage and
> >deploy production-ready predictive services for various kinds of machine
> >learning tasks.
> >
> >Proposal
> >
> >The PredictionIO platform consists of the following components:
> >
> > * PredictionIO framework - provides the machine learning stack for
> > building, evaluating and deploying engines with machine learning
> > algorithms. It uses Apache Spark for processing.
> >
> > * Event Server - the machine learning analytics layer for unifying
> events
> > from multiple platforms. It can use Apache HBase or any JDBC backends
> > as its data store.
> >
> >The PredictionIO community also maintains a Template Gallery, a place to
> >publish and download (free or proprietary) engine templates for different
> >types of machine learning applications, and is a complemental part of the
> >project. At this point we exclude the Template Gallery from the proposal,
> >as it has a separate set of contributors and we’re not familiar with an
> >Apache approved mechanism to maintain such a gallery.
> >
> >Background
> >
> >PredictionIO was started with a mission to democratize and bring machine
> >learning to the masses.
> >
> >Machine learning has traditionally been a luxury for big companies like
> >Google, Facebook, and Netflix. There are ML libraries and tools lying
> >around the internet but the effort of putting them all together as a
> >production-ready infrastructure is a very resource-intensive task that is
> >remotely reachable by individuals or small businesses.
> >
> >PredictionIO is a production-ready, full stack machine learning system
> that
> >allows organizations of any scale to quickly deploy machine learning
> >capabilities. It comes with official and community-contributed machine
> >learning engine templates that are easy to customize.
> >
> >Rationale
> >
> >As usage and number of contributors to PredictionIO has grown bigger and
> >more diverse, we have sought for an independent framework for the project
> >to keep thriving. We believe the Apache foundation is a great fit. Joining
> >Apache would ensure that tried and true processes and procedures are in
> >place for the growing number of organizations interested in contributing
> >to PredictionIO. PredictionIO is also a good fit for the Apache
> foundation.
> >PredictionIO was built on top of several Apache projects (HBase, Spark,
> >Hadoop). We are familiar with the Apache process and believe that the
> >democratic and meritocratic nature of the foundation aligns with the
> >project goals.
> >
> >Initial Goals
> >
> >The initial milestones will be to move the existing codebase to Apache and
> >integrate with the Apache development process. Once this is accomplished,
> >we plan for incremental development and releases that follow the Apache
> >guidelines, as well as growing our developer and user communities.
> >
> >Current Status
> >
> >PredictionIO has undergone nine minor releases and many patches.
> >PredictionIO is being used in production by Salesforce.com as well as many
> >other organizations and apps. The PredictionIO codebase is currently
> >hosted at GitHub, which will form the basis of the Apache git repository.
> >
> >Meritocracy
> >
> >We plan to invest in supporting a meritocracy. We will discuss the
> >requirements in an open forum. We intend to invite additional developers
> >to participate. We will encourage and monitor community participation so
> >that privileges can be extended to those that contribute.
> >
> >Community
> >
> >Acceptance into the Apache foundation would bolster the already strong
> >user and developer community around PredictionIO. That community includes
> >many contributors from various other companies, and an active mailing list
> >composed of hundreds of users.
> >
> >Core Developers
> >
> >The core developers of our project are listed in our contributors and
> >initial PPMC below. Though many are employed at Salesforce.com, there are
> >also engineers from ActionML, and independent developers.
> >
> >Alignment
> >
> >The ASF is the natural choice to host the PredictionIO project as its goal
> >is democratizing Machine Learning by making it more easily accessible to
> >every user/developer. PredictionIO is built on top of several top level
> >Apache projects as outlined above.
> >
> >Known Risks
> >
> >Orphaned Products
> >
> >PredictionIO has a solid and growing community. It is deployed on
> >production environments by companies of all sizes to run various kinds of
> >predictive engines.
> >
> >In addition to the community contribution to PredictionIO framework, the
> >community is also actively contributing new engines to the Template
> >Gallery as well as SDKs and documentation for the project. Salesforce is
> >committed to utilize and advance the PredictionIO code base and support
> >its user community.
> >
> >Inexperience with Open Source
> >
> >PredictionIO has existed as a healthy open source project for almost two
> >years and is the most starred Scala project on GitHub. All of the proposed
> >committers have contributed to ASF and Linux Foundation open source
> >projects. Several current committers on Apache projects and Apache Members
> >are involved in this proposal and intend to provide mentorship.
> >
> >Homogeneous Developers
> >
> >The initial list of committers includes developers from several
> >institutions, including Salesforce, ActionML, Channel4, USC as well as
> >unaffiliated developers.
> >
> >Reliance on Salaried Developers
> >
> >Like most open source projects, PredictionIO receives substantial support
> >from salaried developers. PredictionIO development is partially supported
> >by Salesforce.com, but there are many contributors from various other
> >companies, and an active mailing list composed of hundreds of users. We
> >will continue our efforts to ensure stewardship of the project to be
> >independent of salaried developers by meritocratically promoting those
> >contributors to committers.
> >
> >Relationships with Other Apache Product
> >
> >PredictionIO relies heavily on top level Apache projects such as Apache
> >Spark, HBase and Hadoop. However it brings a distinguished functionality,
> >rather than just an abstraction - Machine Learning in a plug-and-play
> >fashion.
> >
> >Compared to Apache Mahout, which focuses on the development of a wide
> >variety of algorithms, PredictionIO offers a platform to manage the whole
> >machine learning workflow, including data collection, data preparation,
> >modeling, deployment and management of predictive services in production
> >environments.
> >
> >An Excessive Fascination with the Apache Brand
> >
> >PredictionIO is already a widely known open source project. This proposal
> >is not for the purpose of generating publicity. Rather, the primary
> >benefits to joining Apache are those outlined in the Rationale section.
> >
> >Documentation
> >
> >PredictionIO boasts rich and live documentation, included in the code repo
> >(docs/manual directory), is built with Middleman, and publicly hosted at
> >https://docs.prediction.io
> >
> >Initial Source and Intellectual Property Submission Plan
> >
> >Currently, the PredictionIO codebase is distributed under the Apache 2.0
> >License and hosted on GitHub:
> https://github.com/PredictionIO/PredictionIO
> >
> >External Dependencies
> >
> >PredictionIO has the following external dependencies:
> > * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are
> needed)
> > * Apache Spark 1.3.0 for Hadoop 2.4
> > * Java SE Development Kit 8
> > * and one of the following sets:
> > * PostgreSQL 9.1
> > or
> > * MySQL 5.1
> > or
> > * Apache HBase 0.98.6
> > * Elasticsearch 1.4.0
> >
> >Upon acceptance to the incubator, we would begin a thorough analysis of
> >all transitive dependencies to verify this information and introduce
> >license checking into the build and release process by integrating with
> >Apache RAT.
> >
> >Cryptography
> >
> >PredictionIO does not include cryptographic code. We utilize standard
> >JCE and JSSE APIs provided by the Java Runtime Environment.
> >
> >Required Resources
> >
> >We request that following resources be created for the project to use
> >
> >Mailing lists
> >
> > predictionio-private@incubator.apache.org (with moderated
> subscriptions)
> > predictionio-dev
> > predictionio-user
> > predictionio-commits
> >
> > We will migrate the existing PredictionIO mailing lists.
> >
> >Git repository
> >
> > The PredictionIO team would like to use Git for source control, due to
> our
> > current use of GitHub.
> >
> > git://git.apache.org/incubator-predictionio
> >
> >Documentation
> >
> > https://predictionio.incubator.apache.org/docs/
> >
> >JIRA instance
> >
> > PredictionIO currently uses the GitHub issue tracking system associated
> > with its repository:
> https://github.com/PredictionIO/PredictionIO/issues.
> > We will migrate to Apache JIRA.
> >
> > JIRA PREDICTIONIO
> > https://issues.apache.org/jira/browse/PREDICTIONIO
> >
> >Other Resources
> >
> > TravisCI for builds and test running.
> >
> > PredictionIO's documentation, included in the code repo (docs/manual
> > directory), is built with Middleman and publicly hosted at
> > https://docs.prediction.io
> >
> > A blog to drive adoption and excitement at https://blog.prediction.io
> >
> >Initial Committers
> >
> > Pat Ferrell
> > Tamas Jambor
> > Justin Yip
> > Xusen Yin
> > Lee Moon Soo
> > Donald Szeto
> > Kenneth Chan
> > Tom Chan
> > Simon Chan
> > Marco Vivero
> > Matthew Tovbin
> > Yevgeny Khodorkovsky
> > Felipe Oliveira
> > Vitaly Gordon
> > Alex Merritt
> >
> >Affiliations
> >
> > Pat Ferrell - ActionML
> > Tamas Jambor - Channel4
> > Justin Yip - independent
> > Xusen Yin - USC
> > Lee Moon Soo - NFLabs
> > Donald Szeto - Salesforce
> > Kenneth Chan - Salesforce
> > Tom Chan - Salesforce
> > Simon Chan - Salesforce
> > Marco Vivero - Salesforce
> > Matthew Tovbin - Salesforce
> > Yevgeny Khodorkovsky - Salesforce
> > Felipe Oliveira - Salesforce
> > Vitaly Gordon - Salesforce
> > Alex Merritt - ActionML
> >
> >Sponsors
> >
> >Champion
> >
> > Andrew Purtell <apurtell at apache dot org>
> >
> >Nominated Mentors
> >
> > Andrew Purtell <apurtell at apache dot org>
> > James Taylor <jtaylor at apache dot org>
> > Lars Hofhansl <larsh at apache dot org>
> > Suneel Marthi <smarthi at apache dot org>
> > Xiangrui Meng <meng at apache dot org>
> > Luciano Resende <lresende at apache dot org>
> >
> >Sponsoring Entity
> >
> > Apache Incubator PMC
> >
> >
> >--
> >Best regards,
> >
> > - Andy
> >
> >Problems worthy of attack prove their worth by hitting back. - Piet Hein
> >(via Tom White)
>
Re: [VOTE] Accept PredictionIO into the Apache Incubator
Posted by "Debo Dutta (dedutta)" <de...@cisco.com>.
+1
On 5/23/16, 3:22 PM, "Andrew Purtell" <ap...@apache.org> wrote:
>Since discussion on the matter of PredictionIO has died down, I would like
>to call a VOTE
>on accepting PredictionIO into the Apache Incubator.
>
>Proposal: https://wiki.apache.org/incubator/PredictionIO
>
>[ ] +1 Accept PredictionIO into the Apache Incubator
>[ ] +0 Abstain
>[ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
>
>This vote will be open for at least 72 hours.
>
>My vote is +1 (binding)
>
>--
>
>PredictionIO Proposal
>
>Abstract
>
>PredictionIO is an open source Machine Learning Server built on top of
>state-of-the-art open source stack, that enables developers to manage and
>deploy production-ready predictive services for various kinds of machine
>learning tasks.
>
>Proposal
>
>The PredictionIO platform consists of the following components:
>
> * PredictionIO framework - provides the machine learning stack for
> building, evaluating and deploying engines with machine learning
> algorithms. It uses Apache Spark for processing.
>
> * Event Server - the machine learning analytics layer for unifying events
> from multiple platforms. It can use Apache HBase or any JDBC backends
> as its data store.
>
>The PredictionIO community also maintains a Template Gallery, a place to
>publish and download (free or proprietary) engine templates for different
>types of machine learning applications, and is a complemental part of the
>project. At this point we exclude the Template Gallery from the proposal,
>as it has a separate set of contributors and we’re not familiar with an
>Apache approved mechanism to maintain such a gallery.
>
>Background
>
>PredictionIO was started with a mission to democratize and bring machine
>learning to the masses.
>
>Machine learning has traditionally been a luxury for big companies like
>Google, Facebook, and Netflix. There are ML libraries and tools lying
>around the internet but the effort of putting them all together as a
>production-ready infrastructure is a very resource-intensive task that is
>remotely reachable by individuals or small businesses.
>
>PredictionIO is a production-ready, full stack machine learning system that
>allows organizations of any scale to quickly deploy machine learning
>capabilities. It comes with official and community-contributed machine
>learning engine templates that are easy to customize.
>
>Rationale
>
>As usage and number of contributors to PredictionIO has grown bigger and
>more diverse, we have sought for an independent framework for the project
>to keep thriving. We believe the Apache foundation is a great fit. Joining
>Apache would ensure that tried and true processes and procedures are in
>place for the growing number of organizations interested in contributing
>to PredictionIO. PredictionIO is also a good fit for the Apache foundation.
>PredictionIO was built on top of several Apache projects (HBase, Spark,
>Hadoop). We are familiar with the Apache process and believe that the
>democratic and meritocratic nature of the foundation aligns with the
>project goals.
>
>Initial Goals
>
>The initial milestones will be to move the existing codebase to Apache and
>integrate with the Apache development process. Once this is accomplished,
>we plan for incremental development and releases that follow the Apache
>guidelines, as well as growing our developer and user communities.
>
>Current Status
>
>PredictionIO has undergone nine minor releases and many patches.
>PredictionIO is being used in production by Salesforce.com as well as many
>other organizations and apps. The PredictionIO codebase is currently
>hosted at GitHub, which will form the basis of the Apache git repository.
>
>Meritocracy
>
>We plan to invest in supporting a meritocracy. We will discuss the
>requirements in an open forum. We intend to invite additional developers
>to participate. We will encourage and monitor community participation so
>that privileges can be extended to those that contribute.
>
>Community
>
>Acceptance into the Apache foundation would bolster the already strong
>user and developer community around PredictionIO. That community includes
>many contributors from various other companies, and an active mailing list
>composed of hundreds of users.
>
>Core Developers
>
>The core developers of our project are listed in our contributors and
>initial PPMC below. Though many are employed at Salesforce.com, there are
>also engineers from ActionML, and independent developers.
>
>Alignment
>
>The ASF is the natural choice to host the PredictionIO project as its goal
>is democratizing Machine Learning by making it more easily accessible to
>every user/developer. PredictionIO is built on top of several top level
>Apache projects as outlined above.
>
>Known Risks
>
>Orphaned Products
>
>PredictionIO has a solid and growing community. It is deployed on
>production environments by companies of all sizes to run various kinds of
>predictive engines.
>
>In addition to the community contribution to PredictionIO framework, the
>community is also actively contributing new engines to the Template
>Gallery as well as SDKs and documentation for the project. Salesforce is
>committed to utilize and advance the PredictionIO code base and support
>its user community.
>
>Inexperience with Open Source
>
>PredictionIO has existed as a healthy open source project for almost two
>years and is the most starred Scala project on GitHub. All of the proposed
>committers have contributed to ASF and Linux Foundation open source
>projects. Several current committers on Apache projects and Apache Members
>are involved in this proposal and intend to provide mentorship.
>
>Homogeneous Developers
>
>The initial list of committers includes developers from several
>institutions, including Salesforce, ActionML, Channel4, USC as well as
>unaffiliated developers.
>
>Reliance on Salaried Developers
>
>Like most open source projects, PredictionIO receives substantial support
>from salaried developers. PredictionIO development is partially supported
>by Salesforce.com, but there are many contributors from various other
>companies, and an active mailing list composed of hundreds of users. We
>will continue our efforts to ensure stewardship of the project to be
>independent of salaried developers by meritocratically promoting those
>contributors to committers.
>
>Relationships with Other Apache Product
>
>PredictionIO relies heavily on top level Apache projects such as Apache
>Spark, HBase and Hadoop. However it brings a distinguished functionality,
>rather than just an abstraction - Machine Learning in a plug-and-play
>fashion.
>
>Compared to Apache Mahout, which focuses on the development of a wide
>variety of algorithms, PredictionIO offers a platform to manage the whole
>machine learning workflow, including data collection, data preparation,
>modeling, deployment and management of predictive services in production
>environments.
>
>An Excessive Fascination with the Apache Brand
>
>PredictionIO is already a widely known open source project. This proposal
>is not for the purpose of generating publicity. Rather, the primary
>benefits to joining Apache are those outlined in the Rationale section.
>
>Documentation
>
>PredictionIO boasts rich and live documentation, included in the code repo
>(docs/manual directory), is built with Middleman, and publicly hosted at
>https://docs.prediction.io
>
>Initial Source and Intellectual Property Submission Plan
>
>Currently, the PredictionIO codebase is distributed under the Apache 2.0
>License and hosted on GitHub: https://github.com/PredictionIO/PredictionIO
>
>External Dependencies
>
>PredictionIO has the following external dependencies:
> * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are needed)
> * Apache Spark 1.3.0 for Hadoop 2.4
> * Java SE Development Kit 8
> * and one of the following sets:
> * PostgreSQL 9.1
> or
> * MySQL 5.1
> or
> * Apache HBase 0.98.6
> * Elasticsearch 1.4.0
>
>Upon acceptance to the incubator, we would begin a thorough analysis of
>all transitive dependencies to verify this information and introduce
>license checking into the build and release process by integrating with
>Apache RAT.
>
>Cryptography
>
>PredictionIO does not include cryptographic code. We utilize standard
>JCE and JSSE APIs provided by the Java Runtime Environment.
>
>Required Resources
>
>We request that following resources be created for the project to use
>
>Mailing lists
>
> predictionio-private@incubator.apache.org (with moderated subscriptions)
> predictionio-dev
> predictionio-user
> predictionio-commits
>
> We will migrate the existing PredictionIO mailing lists.
>
>Git repository
>
> The PredictionIO team would like to use Git for source control, due to our
> current use of GitHub.
>
> git://git.apache.org/incubator-predictionio
>
>Documentation
>
> https://predictionio.incubator.apache.org/docs/
>
>JIRA instance
>
> PredictionIO currently uses the GitHub issue tracking system associated
> with its repository: https://github.com/PredictionIO/PredictionIO/issues.
> We will migrate to Apache JIRA.
>
> JIRA PREDICTIONIO
> https://issues.apache.org/jira/browse/PREDICTIONIO
>
>Other Resources
>
> TravisCI for builds and test running.
>
> PredictionIO's documentation, included in the code repo (docs/manual
> directory), is built with Middleman and publicly hosted at
> https://docs.prediction.io
>
> A blog to drive adoption and excitement at https://blog.prediction.io
>
>Initial Committers
>
> Pat Ferrell
> Tamas Jambor
> Justin Yip
> Xusen Yin
> Lee Moon Soo
> Donald Szeto
> Kenneth Chan
> Tom Chan
> Simon Chan
> Marco Vivero
> Matthew Tovbin
> Yevgeny Khodorkovsky
> Felipe Oliveira
> Vitaly Gordon
> Alex Merritt
>
>Affiliations
>
> Pat Ferrell - ActionML
> Tamas Jambor - Channel4
> Justin Yip - independent
> Xusen Yin - USC
> Lee Moon Soo - NFLabs
> Donald Szeto - Salesforce
> Kenneth Chan - Salesforce
> Tom Chan - Salesforce
> Simon Chan - Salesforce
> Marco Vivero - Salesforce
> Matthew Tovbin - Salesforce
> Yevgeny Khodorkovsky - Salesforce
> Felipe Oliveira - Salesforce
> Vitaly Gordon - Salesforce
> Alex Merritt - ActionML
>
>Sponsors
>
>Champion
>
> Andrew Purtell <apurtell at apache dot org>
>
>Nominated Mentors
>
> Andrew Purtell <apurtell at apache dot org>
> James Taylor <jtaylor at apache dot org>
> Lars Hofhansl <larsh at apache dot org>
> Suneel Marthi <smarthi at apache dot org>
> Xiangrui Meng <meng at apache dot org>
> Luciano Resende <lresende at apache dot org>
>
>Sponsoring Entity
>
> Apache Incubator PMC
>
>
>--
>Best regards,
>
> - Andy
>
>Problems worthy of attack prove their worth by hitting back. - Piet Hein
>(via Tom White)