Splunk Machine Learning Toolkit

The Splunk Machine Learning Toolkit 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. MLTK Quick Reference Guide: https://docs.splunk.com/images/3/3f/Splunk-MLTK-QuickRefGuide-2019-web.pdf 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 business anomalies to reduce noise. Smart Assistants (new assistants with revamped UI and better ml pipeline/experiment management): * Smart Forecasting Assistant:: e.g. forecasting app logons with special days. * Smart Outlier Detection Assistant: e.g. find anomalies in supermarket purchases. * Smart Clustering Assistant: e.g. cluster houses by property descriptions. * Smart Prediction Assistant: e.g. predict vulnerabilities in firewall data. Available on both on-premises and cloud. (c) Splunk 2024. All rights reserved.