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* [DOCS] Add ML categorization of messages * [DOCS] Describe ML categorization_examples_limit property * [DOCS] Updated ML categorization of messages * [DOCS] Add links to ML categorization Original commit: elastic/x-pack-elasticsearch@6403f6ce84
36 lines
1.3 KiB
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36 lines
1.3 KiB
Plaintext
[[ml-configuring]]
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== Configuring Machine Learning
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If you want to use {xpackml} features, there must be at least one {ml} node in
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your cluster and all master-eligible nodes must have {ml} enabled. By default,
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when you install {xpack}, all nodes are {ml} nodes. For more information about
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these settings, see <<ml-settings>>.
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To use the {xpackml} features to analyze your data, you must create a job and
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send your data to that job.
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* If your data is stored in {es}:
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** You can create a {dfeed}, which retrieves data from {es} for analysis.
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** You can use {kib} to expedite the creation of jobs and {dfeeds}.
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* If your data is not stored in {es}, you can <<ml-post-data,POST data>> from any
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source directly to an API.
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The results of {ml} analysis are stored in {es} and you can use {kib} to help
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you visualize and explore the results.
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For a tutorial that walks you through these configuration steps,
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see <<ml-getting-started>>.
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Though it is quite simple to analyze your data and provide quick {ml} results,
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gaining deep insights might require some additional planning and configuration.
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The scenarios in this section describe some best practices for generating useful
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{ml} results and insights from your data.
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* <<ml-configuring-aggregation>>
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* <<ml-configuring-categories>>
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include::aggregations.asciidoc[]
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include::categories.asciidoc[]
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