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From Domino Valdano <>
Subject [ANNOUNCE] Apache MADlib 1.16 released
Date Tue, 09 Jul 2019 19:39:09 GMT
The Apache MADlib team is pleased to announce the immediate
availability of MADlib version 1.16.

Highlights of the MADlib 1.16 release:

New features:
    - Deep learning: support for Keras with TensorFlow backend with GPU
    - acceleration
    - Deep learning: utility to load model architectures and weights
    - Deep learning: preprocess images for gradient descent optimization
    - kd-tree method for k-nearest neighbors for faster approximate
    - Support for Greenplum 6
    - Support for PostgreSQL 11

Bug fixes, including:
    - Jaccard distance was not releasing memory
    - MLP with minibatching on postgres fixed
    - MLP was not stopping after tolerance was reached
    - MLP warm start fixed
    - MLP with minibatch for integer dependent variable on PostgreSQL
    - Pivot: array_agg/distinct scaling issue on gpdb fixed
    - linregr_train with dependent variable a JSONB element fixed
    - MADLib 1.15 was not recognizing Postgres 10 declarative partitioned
    - Encoding module with bigint fixed
    - SVM class_weight param fixed

    - Simplified maintenance by removing online examples from sql functions
    - Improved performance for weakly connected components
    - SVD and other messaging improvements
    - max itemset size default in assoc rules changed to 10

The complete release notes can be found here:

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

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


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


Thank you, everyone who contributed to the MADlib 1.16 release. We
look forward to continued community participation for the next

Warm Regards,
Domino Valdano

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