[Announce] Apache MADlib v1.12 released

[Ed Espino <es...@apache.org>]

The Apache MADlib team is pleased to announce the immediate
availability of the 1.12 release.

This is the project's initial release as an Apache Top Level
Project.

The main goals of this release are:

* New modules (All Pairs Shortest Path, Weakly Connected Components,
  Breadth First Search, Mulitple Graph Measures, Stratified Sampling,
  Train-test split, Multilayer Perceptron)
* Decision tree and random forest improvements (Allow expressions in
  feature list, Allow array input for features, Filter NULL dependent
  values in OOB, Add option to treat NULL as category)
* Summary improvements (Allow user to determine the number of columns
  per run, Improve efficiency of computation time by ~35%)
* Sketch improvements (Promote cardinality estimators to top level
  module from early stage)
* Add basic code coverage support
* Updates for Apache Top Level Project
* Multiple bug fixes

All release changes can be found here:

  https://cwiki.apache.org/confluence/display/MADLIB/MADlib+1.12

You can download the source release and convenience binary packages
from Apache MADlib's download page here:

  http://madlib.apache.org/download.html

Alternatively, you can download through an ASF mirror near you:

  https://www.apache.org/dyn/closer.lua/madlib/1.12

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Apache MADlib is an open-source library for scalable in-database
analytics. It provides data-parallel implementations of mathematical,
statistical and machine learning methods for structured and
unstructured data.

The MADlib mission: to foster widespread development of scalable
analytic skills, by harnessing efforts from commercial practice,
academic research, and open-source development.

We welcome your help and feedback. For more information on how to
report problems, and to get involved, visit the project website at
https://madlib.apache.org

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Thank you, everyone who contributed to the 1.12 release. We look
forward to continued community participation for the next release,
Apache MADlib v2.0!

Regards,
Ed Espino