Apache MADlib: Big Data Machine Learning in SQL

  • Open source, commercially friendly Apache license
  • For PostgreSQL and Greenplum Database®
  • Powerful machine learning, graph, statistics and analytics for data scientists

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Getting Started with Apache MADlib using Jupyter Notebooks

We have created a library of Jupyter Notebooks to help you get started quickly with MADlib. It includes many commonly used algorithms by data scientists.

 

MADlib 1.17.0 Release

On April 9, 2020, MADlib completed its seventh release as an Apache Software Foundation Top Level Project.

New features include:

  • Deep learning - Model selection framework for Keras with Tensorflow backend with GPU acceleration, for model architecture search and hyperparameter optimization.

  • Deep learning - Support for heterogeneous clusters where GPUs are attached to only certain segment hosts.

  • Deep learning - Support inference for imported models not trained in MADlib ("bring your own model").

  • Deep learning - Support transfer learning for multiple model fit function.

  • Deep learning - Generate model selection table for grid search or random search.

  • Deep learning - Helper function to get GPU type and configuration in a database cluster.

  • k-Means clustering - Select optimal number of centroids using elbow or silhouette methods.

  • PostgreSQL 12 support.

Improvements:

  • Association rules - Add option to set number of posterior rules.

  • Correlation and covariance - Improve memory usage with large number of groups.

  • Deep learning - Improve performance of mini-batch preprocessor and fit functions.

  • Docs - Inprove installation guide on wiki.

  • Graph - SSSP should not show vertices in output table that are unreachable.

  • LDA - Add stopping criteria on perplexity.

You are invited to download the 1.17.0 release and review the release notes. For more details about the new deep learning feature, please refer to the Apache MADlib deep learning notes and the Jupyter notebook examples.

 

MADlib 1.16 Release

On July 8, 2019, MADlib completed its sixth release as an Apache Software Foundation Top Level Project.

New features include:

  • Deep learning - Early stage support for Keras with Tensorflow backend with GPU acceleration. Focus on image classification use cases.

  • Deep learning utilities - Load model architectures and weights, parallel loading of images from NumPy arrays or file system, preprocess images for gradient descent optimization algorithms.

  • Greenplum 6 support.

  • PostgreSQL 11 support.

Improvements:

  • K-nearest neighbors - Improve performance with kd-tree approximate method.

  • Association rules - Set default maximum itemset rules to 10 to reduce runtime.

You are invited to download the 1.16 release and review the release notes. For more details about the new deep learning feature, please refer to the Apache MADlib deep learning notes and the Jupyter notebook examples.

 

MADlib 1.15.1 Release

On Oct 15, 2018, MADlib completed its fifth release as an Apache Software Foundation Top Level Project.

New features include: Ubuntu 16.04 support.

Improvements:

  • Elastic net - Support grouping by non-numeric columns.

  • K-nearest neighbors - Accept expressions for points.

  • Vec2cols - Allow arrays of different lengths.

You are invited to download the 1.15.1 release and review the release notes.

 

MADlib Graduates to Apache Top Level Project

On July 19, 2017, the ASF board established Apache MADlib as a Top Level Project, which was approved by unanimous vote of the directors present. Please see the associated press release from the ASF.

MADlib entered incubation in the fall of 2015 and made five releases as an incubating project. Along the way, the MADlib community has worked hard to ensure that the project is being developed according to the principles of the  The Apache Way. We will continue to do so in the future as a TLP, to the best of our ability.

Thank you to all who have contributed to the project so far, and we look forward more innovation in machine learning in the future as a TLP!