Splunk® Machine Learning Toolkit

User Guide

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Smart Assistants overview

Introduced in version 4.3.0 of the Splunk Machine Learning Toolkit (MLTK), Smart Assistants enable advanced query building and machine learning outcomes for users with little to no Search Processing Language (SPL) knowledge. Built on the backbone of the Experiment Management Framework (EMF), Smart Assistants offer a segmented, guided workflow with an updated user interface. Smart Assistants let you quickly move from fitting a model on historic data to applying a model on real-time data and taking action.

There are currently two Smart Assistants available with more to be released over the coming months:

  • Smart Forecasting Assistant
  • Smart Outlier Detection Assistant

The Smart Assistant workflow

Move through the stages of Define, Learn, Review, and Operationalize to load data, build your model, and put that model into production.

Each stage offers data preview and visualization panels. And as with Experiment Assistants, you get access to modeling history, a method to view the underlying SPL, and the ability to add notes as you work.

This image shows the Smart Forecasting Assistant mid-process. The Define, Learn, and Review stages are all available. The Operationalize stage is greyed out as the Review stage is not yet completed. The image shows a visualization view into the data loaded into the Smart Assistant.

Smart Forecasting Assistant

The Smart Forecasting Assistant offers an updated look and feel as well as well as the option to bring in data from different sources to build your model.

The Smart Forecasting Assistant uses the StateSpaceForecast algorithm to forecast future numeric time-series data. Version 4.4.0 and above of the Smart Forecasting Assistant offers both univariate and multivariate forecasting options.

You can gain familiarity with this new Smart Assistant through the MLTK Showcase, accessed under its own tab. The Showcase examples for Smart Forecasting include:

  • Forecast the Number of Calls to a Call Center

  • Forecast App Logons with Special Days

  • Forecast App Expenses
  • Forecast App Expenses from Multiple Variables

Click the name of any Smart Forecasting Showcase to see this new Assistant and its updated interface using pre-loaded test data and pre-selected forecast parameters.

Smart Outlier Detection Assistant

The Smart Outlier Detection Assistant offers an updated look and feel as well as well as the option to bring in data from different sources to build your model.

The Smart Outlier Detection Assistant uses the DensityFunction algorithm to to leverage a density algorithm and segment data in advance of your anomaly search.

You can gain familiarity with this new Smart Assistant through the MLTK Showcase, accessed under its own tab. The Showcase examples for Smart Outlier Detection include:

  • Find Anomalies in Hard Drive Metrics
  • Find Anomalies in Supermarket Purchases

Click the name of any Smart Outlier Detection Showcase to see this new Assistant and its updated interface using pre-loaded test data and pre-selected outlier detection parameters.

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Experiment Assistants overview

This documentation applies to the following versions of Splunk® Machine Learning Toolkit: 4.5.0, 5.0.0


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