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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About the ML-SPL API

The Splunk Machine Learning Toolkit (MLTK) contains 30 algorithms natively. For users who want to port their own custom algorithm into the MLTK, you can access the machine learning extensibility API.

To add a custom algorithm to the Machine Learning Toolkit, you must write a Python class and register it to the MLTK app. This ML-SPL API Guide covers the process of adding a custom algorithm to the MLTK as well as the option to make that algorithm available to other users through Splunkbase.

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

You can also extend the 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 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 it can be used by other Machine Learning Toolkit users.

Coding knowledge is required to add a custom algorithm to the Machine Learning Toolkit. Advanced Python experience or development experience is also an asset.

Add algorithms using GitHub

On-prem customers looking for solutions that fall outside of the 30 native algorithms can also use GitHub to add more algorithms. Solve custom use 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.

Splunk Cloud Platform customers can also use GitHub to add more algorithms via an app. The Splunk GitHub for Machine learning app provides access to custom algorithms and is based on the Machine Learning Toolkit open source repo. Splunk Cloud Platform customers need to create a support ticket to have this app installed.

To access the Machine Learning Toolkit open source repo, see the MLTK GitHub repo.

The Machine Learning Toolkit and Python for Scientific computing add-on must be installed in order for GitHub to work in your Splunk platform environment.

Last modified on 24 January, 2024
Add a custom algorithm to the Machine Learning Toolkit overview

This documentation applies to the following versions of Splunk® Machine Learning Toolkit: 4.4.0, 4.4.1, 4.4.2, 4.5.0, 5.0.0, 5.1.0, 5.2.0, 5.2.1, 5.2.2, 5.3.0, 5.3.1, 5.3.3

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