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* [Hydrosphere Mist](https://github.com/Hydrospheredata/mist) - a service for deployment Apache Spark MLLib machine learning models as realtime, batch or reactive web services.
* [scikit-learn](https://scikit-learn.org/) - A Python module for machine learning built on top of SciPy.
* [metric-learn](https://github.com/metric-learn/metric-learn) - A Python module for metric learning.
* [Intel(R) Extension for Scikit-learn](https://github.com/intel/scikit-learn-intelex) - A library to speed up your Scikit-learn application.
* [Intel(R) Extension for Scikit-learn](https://github.com/intel/scikit-learn-intelex) - A seamless way to speed up your Scikit-learn applications with no accuracy loss and code changes.
* [SimpleAI](https://github.com/simpleai-team/simpleai) Python implementation of many of the artificial intelligence algorithms described in the book "Artificial Intelligence, a Modern Approach". It focuses on providing an easy to use, well documented and tested library.
* [astroML](https://www.astroml.org/) - Machine Learning and Data Mining for Astronomy.
* [graphlab-create](https://turi.com/products/create/docs/) - A library with various machine learning models (regression, clustering, recommender systems, graph analytics, etc.) implemented on top of a disk-backed DataFrame.