Introduction

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 .

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

On-prem 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.

Cloud 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. Cloud 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 environment.