37 lines
1.5 KiB
Plaintext
37 lines
1.5 KiB
Plaintext
[role="xpack"]
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[[machine-learning-integration]]
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=== Machine Learning integration
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++++
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<titleabbrev>Integrate with machine learning</titleabbrev>
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++++
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The Machine Learning integration initiates a new job predefined to calculate anomaly scores on APM transaction durations.
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Jobs can be created per transaction type, and are based on the service's average response time.
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After a machine learning job is created, results are shown in two places:
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The transaction duration graph will show the expected bounds and add an annotation when the anomaly score is 75 or above.
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[role="screenshot"]
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image::apm/images/apm-ml-integration.png[Example view of anomaly scores on response times in the APM app]
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Service maps will display a color-coded anomaly indicator based on the detected anomaly score.
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[role="screenshot"]
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image::apm/images/apm-service-map-anomaly.png[Example view of anomaly scores on service maps in the APM app]
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[float]
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[[create-ml-integration]]
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=== Create a new machine learning job
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To enable machine learning anomaly detection, first choose a service to monitor.
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Then, select **Integrations** > **Enable ML anomaly detection** and click **Create job**.
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That's it! After a few minutes, the job will begin calculating results;
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it might take additional time for results to appear on your graph.
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Jobs can be managed in *Machine Learning jobs management*.
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APM specific anomaly detection wizards are also available for certain Agents.
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See the machine learning {ml-docs}/ootb-ml-jobs-apm.html[APM anomaly detection configurations] for more information.
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