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++++ Integrate with machine learning ++++ -The Machine Learning integration initiates a new job predefined to calculate anomaly scores on APM transaction durations. -Jobs can be created per transaction type, and are based on the service's average response time. +The Machine learning integration initiates a new job predefined to calculate anomaly scores on APM transaction durations. +With this integration, you can quickly pinpoint anomalous transactions and see the health of +any upstream and downstream services. -After a machine learning job is created, results are shown in two places: +Machine learning jobs are created per environment, and are based on a service's average response time. +Because jobs are created at the environment level, +you can add new services to your existing environments without the need for additional machine learning jobs. -The transaction duration graph will show the expected bounds and add an annotation when the anomaly score is 75 or above. +After a machine learning job is created, results are shown in two places: +* The transaction duration graph will show the expected bounds and add an annotation when the anomaly score is 75 or above. ++ [role="screenshot"] image::apm/images/apm-ml-integration.png[Example view of anomaly scores on response times in the APM app] -Service maps will display a color-coded anomaly indicator based on the detected anomaly score. - +* Service maps will display a color-coded anomaly indicator based on the detected anomaly score. +If there is no anomalous activity detected, ++ [role="screenshot"] image::apm/images/apm-service-map-anomaly.png[Example view of anomaly scores on service maps in the APM app] [float] [[create-ml-integration]] -=== Create a new machine learning job +=== Enable anomaly detection + +To enable machine learning anomaly detection: + +. From the Services overview, Traces overview, or Service Map tab, +select **Anomaly detection**. + +. Click **Create ML Job**. -To enable machine learning anomaly detection, first choose a service to monitor. -Then, select **Integrations** > **Enable ML anomaly detection** and click **Create job**. +. Machine learning jobs are created at the environment level. +Select all of the service environments that you want to enable anomaly detection in. +Anomalies will surface for all services and transaction types within the selected environments. + +. Click **Create Jobs**. That's it! After a few minutes, the job will begin calculating results; -it might take additional time for results to appear on your graph. -Jobs can be managed in *Machine Learning jobs management*. +it might take additional time for results to appear on your service maps. +Existing jobs can be managed in *Machine Learning jobs management*. APM specific anomaly detection wizards are also available for certain Agents. See the machine learning {ml-docs}/ootb-ml-jobs-apm.html[APM anomaly detection configurations] for more information. + +[float] +[[warning-ml-integration]] +=== Anomaly detection warning + +To make machine learning as easy as possible to set up, +the APM app will warn you when filtered to an environment without a machine learning job. + +[role="screenshot"] +image::apm/images/apm-anomaly-alert.png[Example view of anomaly alert in the APM app] \ No newline at end of file From 76759e0573855a97e03f4fe2417f259ca08862ad Mon Sep 17 00:00:00 2001 From: Brandon Morelli Date: Thu, 30 Jul 2020 13:55:34 -0700 Subject: [PATCH 2/2] docs: feedback from @smith --- docs/apm/machine-learning.asciidoc | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/apm/machine-learning.asciidoc b/docs/apm/machine-learning.asciidoc index 23739a94207bf..db2a1ef6e2da0 100644 --- a/docs/apm/machine-learning.asciidoc +++ b/docs/apm/machine-learning.asciidoc @@ -16,13 +16,12 @@ you can add new services to your existing environments without the need for addi After a machine learning job is created, results are shown in two places: -* The transaction duration graph will show the expected bounds and add an annotation when the anomaly score is 75 or above. +* The transaction duration chart will show the expected bounds and add an annotation when the anomaly score is 75 or above. + [role="screenshot"] image::apm/images/apm-ml-integration.png[Example view of anomaly scores on response times in the APM app] * Service maps will display a color-coded anomaly indicator based on the detected anomaly score. -If there is no anomalous activity detected, + [role="screenshot"] image::apm/images/apm-service-map-anomaly.png[Example view of anomaly scores on service maps in the APM app]