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* [Featureforge](https://github.com/machinalis/featureforge) A set of tools for creating and testing machine learning features, with a scikit-learn compatible API.
* [MLlib in Apache Spark](http://spark.apache.org/docs/latest/mllib-guide.html) - Distributed machine learning library in Spark
* [Hydrosphere Mist](https://github.com/Hydrospheredata/mist) - a service for deployment Apache Spark MLLib machine learning models as realtime, batch or reactive web services.
* [Towhee](https://towhee.io) - A Python module that encode unstructured data into embeddings.
* [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 seamless way to speed up your Scikit-learn applications with no accuracy loss and code changes.