Splunk® Machine Learning Toolkit


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This documentation does not apply to the most recent version of Splunk® Machine Learning Toolkit. For documentation on the most recent version, go to the latest release.
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This guide describes how to use the ML-SPL API to import an algorithm for use in the Splunk Machine Learning Toolkit. The ML-SPL API was refactored in the 2.2.0 release to make it easier for developers to import algorithms. This API documentation applies to the Splunk Machine Learning Toolkit version 2.2.0 or later. Verify your Splunk Machine Learning Toolkit version before using this API.

The Splunk Machine Learning Toolkit contains 30 algorithms natively. You can extend the Splunk Machine Learning Toolkit with over 300 open source Python algorithms from scikit-learn, pandas, statsmodel, numpy, and scipy libraries. These open source algorithms are available to the Splunk Machine Learning Toolkit through the Python for Scientific Computing add-on available on Splunkbase. You can also package your custom algorithm as a separate app to share on Splunkbase so that other Splunk Machine Learning Toolkit users can use it.

For information about the algorithms packaged with the Splunk Machine Learning Toolkit, see the Algorithms section in the Splunk Machine Learning Toolkit User Guide .

Customers looking for solutions that fall outside of the 30 native algorithms can use GitHub to add more algorithms. Solve custom uses cases through sharing and reusing algorithms in the Splunk Community for MLTK on GitHub. Here you can also learn about new machine learning algorithms from the Splunk open source community, and help fellow users of the toolkit.

Coding is required to import an algorithm into the Splunk Machine Learning Toolkit, therefore development experience is an asset.

Last modified on 08 March, 2019
Add an algorithm

This documentation applies to the following versions of Splunk® Machine Learning Toolkit: 4.0.0, 4.1.0

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