Splunk Cookie Policy We use our own and third-party cookies to provide you with a great online experience. We also use these cookies to improve our products and services, support our marketing campaigns, and advertise to you on our website and other websites. Some cookies may continue to collect information after you have left our website. Learn more (including how to update your settings) here. Accept Cookie Policy New Splunkbase SPLUNK Splunk Machine Learning Toolkit 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/mltkcheatsheet 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 Smart Assistants (new assistants with revamped UI and better ml pipeline/experiment management): *Smart Forecasting Assistant (provides enhanced time-series analysis for users with little to no SPL knowledge and leverages the StateSpaceForecasting algorithm): e.g. forecasting app logons with special days Available on both on-premise and cloud. Deep Learning Toolkit for Splunk Integrate with advanced custom machine learning systems using the Deep Learning Toolkit for Splunk (https://splunkbase.splunk.com/app/4607/). It extends Splunk’s Machine Learning Toolkit with prebuilt Docker containers for TensorFlow 2.0, PyTorch and a collection of NLP libraries. 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 The GitHub repo algorithms are 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/ Available on cloud and on-premise Splunk Cloud Splunk Built