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‎.backportrc.json‎

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],
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"targetPRLabels": ["backport"],
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"branchLabelMapping": {
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"^v7.8.0$": "7.x",
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"^v7.9.0$": "7.x",
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"^v(\\d+).(\\d+).\\d+$": "$1.$2"
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}
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}

‎.eslintrc.js‎

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files: ['x-pack/plugins/lens/**/*.{js,ts,tsx}'],
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rules: {
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'react-hooks/exhaustive-deps': 'off',
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'react-hooks/rules-of-hooks': 'off',
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},
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},
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{
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],
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from: [
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'(src|x-pack)/plugins/**/(public|server)/**/*',
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'!(src|x-pack)/plugins/**/(public|server)/mocks/index.{js,ts}',
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'!(src|x-pack)/plugins/**/(public|server)/(index|mocks).{js,ts,tsx}',
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],
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allowSameFolder: true,

‎.i18nrc.json‎

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"kibana_utils": "src/plugins/kibana_utils",
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"navigation": "src/plugins/navigation",
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"newsfeed": "src/plugins/newsfeed",
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"regionMap": "src/legacy/core_plugins/region_map",
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"regionMap": "src/plugins/region_map",
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"savedObjects": "src/plugins/saved_objects",
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"savedObjectsManagement": "src/plugins/saved_objects_management",
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"server": "src/legacy/server",
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"src/plugins/telemetry",
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"src/plugins/telemetry_management_section"
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],
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"tileMap": "src/legacy/core_plugins/tile_map",
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"tileMap": "src/plugins/tile_map",
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"timelion": ["src/legacy/core_plugins/timelion", "src/plugins/vis_type_timelion"],
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"uiActions": "src/plugins/ui_actions",
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"visDefaultEditor": "src/plugins/vis_default_editor",

‎docs/apm/images/apm-service-map-anomaly.png‎

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‎docs/apm/images/green-service.png‎

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‎docs/apm/images/red-service.png‎

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‎docs/apm/images/service-maps.png‎

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‎docs/apm/images/yellow-service.png‎

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‎docs/apm/machine-learning.asciidoc‎

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<titleabbrev>Integrate with machine learning</titleabbrev>
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++++
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The Machine Learning integration will initiate a new job predefined to calculate anomaly scores on transaction response times.
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The response time graph will show the expected bounds and add an annotation when the anomaly score is 75 or above.
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Jobs can be created per transaction type, and based on the average response time.
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Manage jobs in the *Machine Learning jobs management*.
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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-ml-integration.png[Example view of anomaly scores on response times in APM app in Kibana]
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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.

‎docs/apm/service-maps.asciidoc‎

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A service map is a real-time visual representation of the instrumented services in your application's architecture.
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It shows you how these services are connected, along with high-level metrics like average transaction duration,
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requests per minute, and errors per minute, that allow you to quickly assess the status of your services.
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requests per minute, and errors per minute.
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If enabled, service maps also integrate with machine learning--for real time health indicators based on anomaly detection scores.
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All of these features can help you to quickly and visually assess the status and health of your services.
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We currently surface two types of service maps:
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[role="screenshot"]
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image::apm/images/service-maps-java.png[Example view of service maps with Java highlighted in the APM app in Kibana]
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[float]
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[[service-map-anomaly-detection]]
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=== Anomaly detection with machine learning
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Machine learning jobs can be created to calculate anomaly scores on APM transaction durations within the selected service.
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When these jobs are active, service maps will display a color-coded anomaly indicator based on the detected anomaly score:
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[horizontal]
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image:apm/images/green-service.png[APM green service]:: Max anomaly score **<=25**. Service is healthy.
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image:apm/images/yellow-service.png[APM yellow service]:: Max anomaly score **26-74**. Anomalous activity detected. Service may be degraded.
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image:apm/images/red-service.png[APM red service]:: Max anomaly score **>=75**. Anomalous activity detected. Service is unhealthy.
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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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If an anomaly has been detected, click *view anomalies* to view the anomaly detection metric viewier in the Machine learning app.
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This time series analysis will display additional details on the severity and time of the detected anomalies.
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To learn how to create a machine learning job, see <<machine-learning-integration,machine learning integration>>.
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[float]
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[[service-maps-legend]]
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=== Legend

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