[apm] Update machine learning flyout and service maps docs (#65517)

Author: bmorelli25

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

@@ -6,22 +6,31 @@
 <titleabbrev>Integrate with machine learning</titleabbrev>
 ++++
 
-The Machine Learning integration will initiate a new job predefined to calculate anomaly scores on transaction response times.
-The response time graph will show the expected bounds and add an annotation when the anomaly score is 75 or above.
-Jobs can be created per transaction type, and based on the average response time.
-Manage jobs in the *Machine Learning jobs management*.
+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.
+
+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.
 
 [role="screenshot"]
-image::apm/images/apm-ml-integration.png[Example view of anomaly scores on response times in APM app in Kibana]
+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
 
 To enable machine learning anomaly detection, first choose a service to monitor.
 Then, select **Integrations** > **Enable ML anomaly detection** and click **Create job**.
+
 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*.
 
 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.

docs/apm/images/green-service.png

@@ -9,7 +9,9 @@ Please use Chrome or Firefox if available.
 
 A service map is a real-time visual representation of the instrumented services in your application's architecture.
 It shows you how these services are connected, along with high-level metrics like average transaction duration,
-requests per minute, and errors per minute, that allow you to quickly assess the status of your services.
+requests per minute, and errors per minute.
+If enabled, service maps also integrate with machine learning--for real time health indicators based on anomaly detection scores.
+All of these features can help you to quickly and visually assess the status and health of your services.
 
 We currently surface two types of service maps:
 
@@ -52,6 +54,26 @@ Additional filters are not currently available for service maps.
 [role="screenshot"]
 image::apm/images/service-maps-java.png[Example view of service maps with Java highlighted in the APM app in Kibana]
 
+[float]
+[[service-map-anomaly-detection]]
+=== Anomaly detection with machine learning
+
+Machine learning jobs can be created to calculate anomaly scores on APM transaction durations within the selected service.
+When these jobs are active, service maps will display a color-coded anomaly indicator based on the detected anomaly score:
+
+[horizontal]
+image:apm/images/green-service.png[APM green service]:: Max anomaly score **<=25**. Service is healthy.
+image:apm/images/yellow-service.png[APM yellow service]:: Max anomaly score **26-74**. Anomalous activity detected. Service may be degraded.
+image:apm/images/red-service.png[APM red service]:: Max anomaly score **>=75**. Anomalous activity detected. Service is unhealthy.
+
+[role="screenshot"]
+image::apm/images/apm-service-map-anomaly.png[Example view of anomaly scores on service maps in the APM app]
+
+If an anomaly has been detected, click *view anomalies* to view the anomaly detection metric viewier in the Machine learning app.
+This time series analysis will display additional details on the severity and time of the detected anomalies.
+
+To learn how to create a machine learning job, see <<machine-learning-integration,machine learning integration>>.
+
 [float]
 [[service-maps-legend]]
 === Legend

docs/apm/images/red-service.png

@@ -134,17 +134,29 @@ export function MachineLearningFlyoutView({
           <p>
             <FormattedMessage
               id="xpack.apm.serviceDetails.enableAnomalyDetectionPanel.createMLJobDescription"
-              defaultMessage="Here you can create a machine learning job to calculate anomaly scores on durations for APM transactions
-                    within the {serviceName} service. Once enabled, {transactionDurationGraphText} will show the expected bounds and annotate
-                    the graph once the anomaly score is &gt;=75."
+              defaultMessage="Create a machine learning job to calculate anomaly scores on APM transaction durations
+                    within the {serviceName} service. When enabled, anomalies are show in two places:
+                    The {transactionDurationGraphText} graph will show the expected bounds and annotate
+                    the graph if the anomaly score is &gt;=75, and {serviceMapAnnotationText} will display color
+                    coded service indicators based on the active anomaly score."
               values={{
                 serviceName,
                 transactionDurationGraphText: (
                   <b>
                     {i18n.translate(
                       'xpack.apm.serviceDetails.enableAnomalyDetectionPanel.createMLJobDescription.transactionDurationGraphText',
                       {
-                        defaultMessage: 'the transaction duration graph'
+                        defaultMessage: 'transaction duration'
+                      }
+                    )}
+                  </b>
+                ),
+                serviceMapAnnotationText: (
+                  <b>
+                    {i18n.translate(
+                      'xpack.apm.serviceDetails.enableAnomalyDetectionPanel.createMLJobDescription.serviceMapAnnotationText',
+                      {
+                        defaultMessage: 'service maps'
                       }
                     )}
                   </b>
@@ -155,15 +167,15 @@ export function MachineLearningFlyoutView({
           <p>
             <FormattedMessage
               id="xpack.apm.serviceDetails.enableAnomalyDetectionPanel.manageMLJobDescription"
-              defaultMessage="Jobs can be created for each service + transaction type combination.
-                    Once a job is created, you can manage it and see more details in the {mlJobsPageLink}."
+              defaultMessage="Jobs can be created for each service and transaction type.
+                    Once a job is created, you can manage it and see more details on the {mlJobsPageLink}."
               values={{
                 mlJobsPageLink: (
                   <MLLink>
                     {i18n.translate(
                       'xpack.apm.serviceDetails.enableAnomalyDetectionPanel.manageMLJobDescription.mlJobsPageLinkText',
                       {
-                        defaultMessage: 'Machine Learning jobs management page'
+                        defaultMessage: 'Machine Learning Job Management page'
                       }
                     )}
                   </MLLink>

docs/apm/images/service-maps.png

@@ -4314,7 +4314,6 @@
     "xpack.apm.serviceDetails.enableAnomalyDetectionPanel.callout.jobExistsDescription": "現在 {serviceName} ({transactionType}) の実行中のジョブがあります。",
     "xpack.apm.serviceDetails.enableAnomalyDetectionPanel.callout.jobExistsDescription.viewJobLinkText": "既存のジョブを表示",
     "xpack.apm.serviceDetails.enableAnomalyDetectionPanel.callout.jobExistsTitle": "ジョブが既に存在します",
-    "xpack.apm.serviceDetails.enableAnomalyDetectionPanel.createMLJobDescription": "ここでは、{serviceName} 数列内の APM トランザクションの期間の異常スコアを計算する機械学習ジョブを作成できます。有効にすると、{transactionDurationGraphText} が予測バウンドを表示し、異常スコアが >=75 の場合グラフに注釈が追加されます。",
     "xpack.apm.serviceDetails.enableAnomalyDetectionPanel.createMLJobDescription.transactionDurationGraphText": "トランザクション時間のグラフ",
     "xpack.apm.serviceDetails.enableAnomalyDetectionPanel.createNewJobButtonLabel": "ジョブを作成",
     "xpack.apm.serviceDetails.enableAnomalyDetectionPanel.enableAnomalyDetectionTitle": "異常検知を有効にする",

docs/apm/images/yellow-service.png

@@ -4315,7 +4315,6 @@
     "xpack.apm.serviceDetails.enableAnomalyDetectionPanel.callout.jobExistsDescription": "当前有 {serviceName}（{transactionType}）的作业正在运行。",
     "xpack.apm.serviceDetails.enableAnomalyDetectionPanel.callout.jobExistsDescription.viewJobLinkText": "查看现有作业",
     "xpack.apm.serviceDetails.enableAnomalyDetectionPanel.callout.jobExistsTitle": "作业已存在",
-    "xpack.apm.serviceDetails.enableAnomalyDetectionPanel.createMLJobDescription": "在这里可以创建 Machine Learning 作业以基于 {serviceName} 服务内 APM 事务的持续时间计算异常分数。启用后，一旦异常分数 >=75，{transactionDurationGraphText}将显示预期边界并标注图表。",
     "xpack.apm.serviceDetails.enableAnomalyDetectionPanel.createMLJobDescription.transactionDurationGraphText": "事务持续时间图表",
     "xpack.apm.serviceDetails.enableAnomalyDetectionPanel.createNewJobButtonLabel": "创建作业",
     "xpack.apm.serviceDetails.enableAnomalyDetectionPanel.enableAnomalyDetectionTitle": "启用异常检测",