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Downloading Splunk Machine Learning Toolkit
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Splunk Machine Learning Toolkit

Splunk Built
Splunk Machine Learning Toolkit

The Splunk Machine Learning Toolkit App delivers new SPL commands, custom visualizations, assistants, and examples to explore a variety of ml concepts.

Each assistant includes end-to-end examples with datasets, plus the ability to apply the visualizations and SPL commands to your own data. You can inspect the assistant panels and underlying code to see how it all works.

ML Youtube Playlist http://tiny.cc/splunkmlvideos
ML Cheat Sheet http://tiny.cc/mlcheatsheet

* Predict Numeric Fields (Linear Regression): e.g. predict median house values.
* Predict Categorical Fields (Logistic Regression): e.g. predict customer churn.
* Detect Numeric Outliers (distribution statistics): e.g. detect outliers in IT Ops data.
* Detect Categorical Outliers (probabilistic measures): e.g. detect outliers in diabetes patient records.
* Forecast Time Series: e.g. forecast data center growth and capacity planning.
* Cluster Numeric Events: e.g. Cluster Hard Drives by SMART Metrics

Available on both on-premise and cloud.

Splunk Machine Learning Toolkit Connector for Apache Spark™
The limited availability release of Splunk MTLK Connector for Apache Spark™ is available via the Splunk Beta Portal for on-prem users. It allows users to leverage their own Spark clusters to train machine learning models on large data sets using the Spark infrastructure as opposed to the Splunk search head. It also provides easier scaling, high elasticity, and access to MLlib algorithms.

Email us at sparkml@splunk.com if you want to participate in a program which would help you with the installation and setup process of the connector or for any other query.
Available only for on-premise customers.

Splunk MLTK Container for TensorFlow™
Access the TensorFlow™ library through the Splunk MLTK Container for TensorFlow™ available through certified Splunk Professional Services. The container leverages your bespoke GPU hardware , connecting from your on-premise Splunk Enterprise deployment to run custom deep learning from SPL . Python expertise is required to create your own neural networks.
Available only for on-premise customers.

Splunk Community for MLTK Algorithms on GitHub
Check out our Open Source community on Github that lets you share your algorithms with the community of Splunk MLTK users or import one of the algorithms that have been shared by the community: https://github.com/splunk/mltk-algo-contrib
Available only for on-premise customers.

The GitHub repo algorithms is also available as an app which provides access to custom algorithms. Cloud customers can use GitHub algorithms via this app and need to create a support ticket to have this installed:https://splunkbase.splunk.com/app/4403/

Signup for MLTK Beta Program to get early access to new features in upcoming releases: http://tiny.cc/mltkbeta

For the Splunk Machine Learning Toolkit documention, see: http://docs.splunk.com/Documentation/MLApp/latest


This application may contain certain sample files and datasets, which are provided for your convenience only. Such files and datasets contain information and data compiled by third parties, and Splunk makes no representation or warranty that the data contained in such files and datasets are true, accurate, complete or sanitized.


You must install the Python for Scientific Computing Add-on before installing the Machine Learning Toolkit. Please download and install the appropriate version here:


To install an app within Splunk Enterprise:

  1. Log into Splunk Enterprise.
  2. Next to the Apps menu, click the Manage Apps icon.
  3. Click Install app from file.
  4. In the Upload app dialog box, click Choose File.
  5. Locate the .tar.gz or .tar file you just downloaded, then click Open or Choose.
  6. Click Upload.

Release Notes

Version 4.2.0
March 12, 2019

For the latest release notes, see http://docs.splunk.com/Documentation/MLApp/latest/User/Whatsnew

Version 4.1.0
Dec. 3, 2018

For the latest release notes, see http://docs.splunk.com/Documentation/MLApp/latest/User/Whatsnew

Version 4.0.0
Sept. 26, 2018

For the latest release notes, see http://docs.splunk.com/Documentation/MLApp/latest/User/Whatsnew

Version 3.4.0
Aug. 6, 2018

For the latest release notes, see http://docs.splunk.com/Documentation/MLApp/latest/User/Whatsnew

Version 3.3.0
June 20, 2018

For the latest release notes, see http://docs.splunk.com/Documentation/MLApp/latest/User/Whatsnew

Version 3.2.0
April 20, 2018

For the latest release notes, see http://docs.splunk.com/Documentation/MLApp/latest/User/Whatsnew

Version 3.1.0
Dec. 9, 2017

For the latest release notes, see http://docs.splunk.com/Documentation/MLApp/latest/User/Whatsnew

Version 3.0.0
Oct. 12, 2017

see: https://docs.splunk.com/Documentation/MLApp/3.0.0/User/Whatsnew

Version 2.4.0
Aug. 30, 2017


Version 2.3.0
June 29, 2017


Version 2.2.1
June 17, 2017


Version 2.2.0
April 20, 2017

Video Overview of What's new in Splunk Machine Learning Toolkit 2.1 and 2.2 -> http://tiny.cc/splunkmlupdate

Version 2.1.0
March 3, 2017


Version 2.0.1
Dec. 15, 2016


Version 2.0.0
Sept. 23, 2016


Version 1.3.0
Aug. 8, 2016

For the latest release notes, see http://docs.splunk.com/Documentation/MLApp/latest/User/Whatsnew.

Version 1.2
July 14, 2016

For the latest release notes, see http://docs.splunk.com/Documentation/MLApp/latest/User/Whatsnew.

Version 1.1
May 23, 2016

For the latest release notes, see http://docs.splunk.com/Documentation/MLApp/latest/User/Whatsnew.

Version 1.0
March 29, 2016

Revamped interface with more example datasets relevant to core Splunk use-cases.

Major improvements to the fit and apply search commands:
* Vastly expanded library of algorithms, including the addition of Random Forest, Naive Bayes, and TF-IDF preprocessing for text analytics.
* Supports training models on more than 50,000 events.
* Supports Search Head Clustering.

Version 0.9.2
Dec. 4, 2015


Version 0.9.1
Oct. 19, 2015

Bug fix release.


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