Splunk® Machine Learning Toolkit

ML-SPL API Guide

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.

Custom algorithms using PSC libraries

As with versions 3.4.0 and 4.0.0, upgrading to version 4.1.0 of the toolkit requires upgrading to version 1.3 of the Python for Scientific Computing library.

Two previous versions of the MLTK (3.2.0 and 3.3.0) will successfully operate on versions 1.2 or 1.3 of the Python for Scientific Computing add-on. However, users cannot access new features in the 3.4.0 release and above without upgrading to that version. Version 4.1.0 of the toolkit requires the upgrade to version 1.3 of PSC.

If you have written any custom algorithms that rely on the PSC libraries, upgrading to the new version of the PSC library will impact those algorithms. You will need to re-train any models (re-run the search that used the fit command) using those algorithms after you upgrade PSC.

Specific version dependencies:

MLTK Version PSC Version
4.1 1.3
4.0 1.3
3.4 1.3
3.3 1.2 or 1.3
3.2 1.2 or 1.3
3.1 1.2

If you are still stuck, try posting your question on Splunk Answers.

Last modified on 03 July, 2019
Custom logging   Best Practices

This documentation applies to the following versions of Splunk® Machine Learning Toolkit: 4.1.0, 4.2.0, 4.3.0

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