![]() Assistants are used with your own data and generate Splunk Search Processing Language (SPL) for you. Each Assistant offers a choice of algorithms to fit and apply a model, with visualizations to help you interpret the results. Assistants bring all aspects of a monitored machine learning pipeline into one interface and include automated model versioning and lineage. Guided modeling Assistants to manage your data source, selected algorithm, and any additional parameters used to configure that algorithm.For a detailed look at the Showcases, see Showcase examples. Filter the available Showcases by machine learning operation or industry to see the examples that best match your machine learning goals. Each end-to-end example pre-populates a guided modeling Assistant to demonstrate how to perform different types of machine learning analysis and prediction using best practices, including what the ideal results look like when you're using your own data. A Showcase of different sample datasets to help new users explore machine-learning concepts.The following features are available in the Splunk Machine Learning Toolkit: You must have domain knowledge, Splunk Search Processing Language (SPL) knowledge, Splunk platform experience, and data science skills or experience to use MLTK. The Splunk Machine Learning Toolkit is not a default solution, but a way to create custom machine learning. The Splunk Machine Learning Toolkit (MLTK) enables users to create, validate, manage, and operationalize machine learning models through a guided user interface. These generalizations, typically called models, are used to perform a variety of tasks, such as predicting the value of a field, forecasting future values, identifying patterns in data, and detecting anomalies from new data. Machine learning is a process for generalizing from examples. About the Splunk Machine Learning Toolkit
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