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Splunk Machine Learning Toolkit

Splunk Built
Overview
Details
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

Assistants:
* 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

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

Datasets

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.

Requirements

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

Installation

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 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

http://docs.splunk.com/Documentation/MLApp/2.4.0/User/Whatsnew

Version 2.3.0
June 29, 2017

http://docs.splunk.com/Documentation/MLApp/2.3.0/User/Whatsnew

Version 2.2.1
June 17, 2017

http://docs.splunk.com/Documentation/MLApp/2.2.1/User/Fixedissues

Version 2.2.0
April 20, 2017

http://docs.splunk.com/Documentation/MLApp/2.2.0/User/Whatsnew
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

http://docs.splunk.com/Documentation/MLApp/2.1.0/User/Whatsnew

Version 2.0.1
Dec. 15, 2016

http://docs.splunk.com/Documentation/MLApp/2.0.1/User/Whatsnew

Version 2.0.0
Sept. 23, 2016

http://docs.splunk.com/Documentation/MLApp/2.0.0/User/Whatsnew

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

https://github.com/splunk/ml-toolkit-docs/wiki/Release-Notes

Version 0.9.1
Oct. 19, 2015

Bug fix release.

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