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Posted to issues@ambari.apache.org by "Ananda Verma (JIRA)" <ji...@apache.org> on 2016/10/18 05:52:58 UTC
[jira] [Updated] (AMBARI-18622) Integrate PredictionIO (Machine
Learning Engine) With Ambari
[ https://issues.apache.org/jira/browse/AMBARI-18622?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Ananda Verma updated AMBARI-18622:
----------------------------------
Description:
It makes sense to integrate PredictionIO with Ambari since it is now part of apache group and also heavily depends on current amabri/hdp stack.
Feature includes adding support for apache predictionIO cluster provisioning via Ambari.
In general, pio can be defined as a service in HDP which has following components -
1) Event Server - stores events (data)
2) Engine - Engine is responsible for making prediction. It contains one or more machine learning algorithms. An engine reads training data and build predictive model(s). It is then deployed as a web service. A deployed engine responds to prediction queries from your application through REST API in real-time.
PredictionIO also has external dependencies on following -
1. HBase: Event Server uses Apache HBase as the data store. It stores imported events. If you are not using the PredictionIO Event Server, you do not need to install HBase.
2. Apache Spark: Spark is a large-scale data processing engine that powers the algorithm, training, and serving processing.
3. HDFS: The output of training has two parts: a model and its meta-data. The model is then stored in HDFS or a local file system.
4. Elasticsearch: It stores metadata such as model versions, engine versions, access key and app id mappings, evaluation results, etc.
was:
Feature includes adding support for apache predictionIO cluster provisioning via Ambari.
In general, pio can be defined as a service in HDP which has following components -
1) Event Server - stores events (data)
2) Engine - Engine is responsible for making prediction. It contains one or more machine learning algorithms. An engine reads training data and build predictive model(s). It is then deployed as a web service. A deployed engine responds to prediction queries from your application through REST API in real-time.
PredictionIO also has external dependencies on following -
1. HBase: Event Server uses Apache HBase as the data store. It stores imported events. If you are not using the PredictionIO Event Server, you do not need to install HBase.
2. Apache Spark: Spark is a large-scale data processing engine that powers the algorithm, training, and serving processing.
3. HDFS: The output of training has two parts: a model and its meta-data. The model is then stored in HDFS or a local file system.
4. Elasticsearch: It stores metadata such as model versions, engine versions, access key and app id mappings, evaluation results, etc.
> Integrate PredictionIO (Machine Learning Engine) With Ambari
> ------------------------------------------------------------
>
> Key: AMBARI-18622
> URL: https://issues.apache.org/jira/browse/AMBARI-18622
> Project: Ambari
> Issue Type: New Feature
> Components: ambari-server
> Affects Versions: 2.1.0
> Reporter: Ananda Verma
>
> It makes sense to integrate PredictionIO with Ambari since it is now part of apache group and also heavily depends on current amabri/hdp stack.
> Feature includes adding support for apache predictionIO cluster provisioning via Ambari.
> In general, pio can be defined as a service in HDP which has following components -
> 1) Event Server - stores events (data)
> 2) Engine - Engine is responsible for making prediction. It contains one or more machine learning algorithms. An engine reads training data and build predictive model(s). It is then deployed as a web service. A deployed engine responds to prediction queries from your application through REST API in real-time.
> PredictionIO also has external dependencies on following -
> 1. HBase: Event Server uses Apache HBase as the data store. It stores imported events. If you are not using the PredictionIO Event Server, you do not need to install HBase.
> 2. Apache Spark: Spark is a large-scale data processing engine that powers the algorithm, training, and serving processing.
> 3. HDFS: The output of training has two parts: a model and its meta-data. The model is then stored in HDFS or a local file system.
> 4. Elasticsearch: It stores metadata such as model versions, engine versions, access key and app id mappings, evaluation results, etc.
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