52 lines
1.7 KiB
Plaintext
52 lines
1.7 KiB
Plaintext
[role="xpack"]
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[[ml-configuring]]
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== Configuring machine learning
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If you want to use {ml-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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all nodes are {ml} nodes. For more information about these settings, see
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{ref}/modules-node.html#ml-node[{ml} nodes].
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To use the {ml-features} to analyze your data, you can create an {anomaly-job}
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and 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
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{ref}/ml-post-data.html[POST data] from any 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-url>>
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* <<ml-configuring-aggregation>>
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* <<ml-configuring-categories>>
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* <<ml-configuring-detector-custom-rules>>
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* <<ml-configuring-pop>>
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* <<ml-configuring-transform>>
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* <<ml-delayed-data-detection>>
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include::customurl.asciidoc[]
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include::aggregations.asciidoc[]
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include::detector-custom-rules.asciidoc[]
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include::categories.asciidoc[]
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include::populations.asciidoc[]
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include::transforms.asciidoc[]
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include::delayed-data-detection.asciidoc[] |