Merge pull request #2141 from JKirsch1/update-article-about-monitoring-online-endpoints

Freshness - Machine Learning HowTo 180 days

Author: v-dirichards

articles/machine-learning/how-to-authenticate-online-endpoint.md

@@ -728,7 +728,7 @@ The __Test__ tab of the deployment's detail page supports scoring for endpoints
 
 ## Log and monitor traffic
 
-To enable traffic logging in the diagnostics settings for the endpoint, follow the steps in [How to enable/disable logs](how-to-monitor-online-endpoints.md#how-to-enabledisable-logs).
+To enable traffic logging in the diagnostics settings for the endpoint, follow the steps in [Turn on logs](how-to-monitor-online-endpoints.md#turn-on-logs).
 
 If the diagnostic setting is enabled, you can check the `AmlOnlineEndpointTrafficLogs` table to see the auth mode and user identity.
 

articles/machine-learning/how-to-autoscale-endpoints.md

@@ -455,7 +455,10 @@ The following steps continue the rule configuration on the __Custom autoscale__
 
 ## Find IDs for supported metrics
 
-If you want to use other metrics in code to set up autoscale rules by using the Azure CLI or the SDK, see the table in [Available metrics](how-to-monitor-online-endpoints.md#available-metrics).
+You can use other metrics when you use the Azure CLI or the SDK to set up autoscale rules.
+
+- For the names of endpoint metrics to use in code, see the values in the __Name in REST API__ column in the table in [Supported metrics for Microsoft.MachineLearningServices/workspaces/onlineEndpoints](monitor-azure-machine-learning-reference.md#supported-metrics-for-microsoftmachinelearningservicesworkspacesonlineendpoints).
+- For the names of deployment metrics to use in code, see the values in the __Name in REST API__ column in the tables in [Supported metrics for Microsoft.MachineLearningServices/workspaces/onlineEndpoints/deployments](monitor-azure-machine-learning-reference.md#supported-metrics-for-microsoftmachinelearningservicesworkspacesonlineendpointsdeployments).
 
 ## Create scale rule based on schedule
 

articles/machine-learning/how-to-deploy-online-endpoints.md

@@ -1230,7 +1230,7 @@ To view metrics and set alerts based on your SLA, complete the steps that are de
 
 ### (Optional) Integrate with Log Analytics
 
-The `get-logs` command for CLI or the `get_logs` method for SDK provides only the last few hundred lines of logs from an automatically selected instance. However, Log Analytics provides a way to durably store and analyze logs. For more information on using logging, see [Monitor online endpoints](how-to-monitor-online-endpoints.md#logs).
+The `get-logs` command for CLI or the `get_logs` method for SDK provides only the last few hundred lines of logs from an automatically selected instance. However, Log Analytics provides a way to durably store and analyze logs. For more information on using logging, see [Monitor online endpoints](how-to-monitor-online-endpoints.md#use-logs).
 
 <!-- [!INCLUDE [Email Notification Include](includes/machine-learning-email-notifications.md)] -->
 

articles/machine-learning/how-to-manage-quotas.md

@@ -123,7 +123,7 @@ Azure Machine Learning online endpoints and batch endpoints have resource limits
 > [!IMPORTANT]
 > These limits are *regional*, meaning that you can use up to these limits per each region you're using. For example, if your current limit for number of endpoints per subscription is 100, you can create 100 endpoints in the East US region, 100 endpoints in the West US region, and 100 endpoints in each of the other supported regions in a single subscription. Same principle applies to all the other limits.
 
-To determine the current usage for an endpoint, [view the metrics](how-to-monitor-online-endpoints.md#metrics). 
+To determine the current usage for an endpoint, [view the metrics](how-to-monitor-online-endpoints.md#use-metrics). 
 
 To request an exception from the Azure Machine Learning product team, use the steps in the [Endpoint limit increases](#endpoint-limit-increases).
 

articles/machine-learning/how-to-monitor-online-endpoints.md

@@ -577,7 +577,7 @@ Mirroring has the following limitations:
 * Mirroring is supported for the CLI (v2) (version 2.4.0 or above) and Python SDK (v2) (version 1.0.0 or above). If you use an older version of CLI/SDK to update an endpoint, you'll lose the mirror traffic setting.
 * Mirroring isn't currently supported for Kubernetes online endpoints.
 * You can mirror traffic to only one deployment in an endpoint.
-* The maximum percentage of traffic you can mirror is 50%. This limit is to reduce the effect on your [endpoint bandwidth quota](how-to-manage-quotas.md#azure-machine-learning-online-endpoints-and-batch-endpoints) (default 5 MBPS)—your endpoint bandwidth is throttled if you exceed the allocated quota. For information on monitoring bandwidth throttling, see [Monitor managed online endpoints](how-to-monitor-online-endpoints.md#metrics-at-endpoint-scope).
+* The maximum percentage of traffic you can mirror is 50%. This limit is to reduce the effect on your [endpoint bandwidth quota](how-to-manage-quotas.md#azure-machine-learning-online-endpoints-and-batch-endpoints) (default 5 MBPS)—your endpoint bandwidth is throttled if you exceed the allocated quota. For information on monitoring bandwidth throttling, see [Monitor managed online endpoints](how-to-monitor-online-endpoints.md#metrics-at-the-endpoint-scope).
 
 Also note the following behaviors:
 

articles/machine-learning/how-to-safely-rollout-online-endpoints.md

@@ -2,15 +2,15 @@
 author: Blackmist
 ms.service: azure-machine-learning
 ms.topic: include
-ms.date: 04/22/2023
+ms.date: 01/02/2025
 ms.author: larryfr
 ---
 
 | Property | Description |
 |:--- |:--- |
-| TimeGenerated | The timestamp (UTC) of when the log was generated. 
-| OperationName | The operation associated with log record. 
-| InstanceId | The ID of the instance that generated this log record. 
-| DeploymentName | The name of the deployment associated with the log record. 
-| ContainerName | The name of the container where the log was generated. 
-| Message | The content of the log. 
+| TimeGenerated | The UTC time stamp of the time the log is generated |
+| OperationName | The operation associated with the log record |
+| InstanceId | The ID of the instance that generates the log record |
+| DeploymentName | The name of the deployment associated with the log record |
+| ContainerName | The name of the container where the log is generated |
+| Message | The content of the log |

articles/machine-learning/includes/endpoint-monitor-console-reference.md

@@ -2,15 +2,15 @@
 author: Blackmist
 ms.service: azure-machine-learning
 ms.topic: include
-ms.date: 04/22/2023
+ms.date: 01/02/2025
 ms.author: larryfr
 ---
 
 | Property | Description |
 |:--- |:--- |
-| TimeGenerated | The timestamp (UTC) of when the log was generated. 
-| OperationName | The operation associated with log record. 
-| InstanceId | The ID of the instance that generated this log record. 
-| DeploymentName | The name of the deployment associated with the log record. 
-| Name | The name of the event. 
-| Message | The content of the event. 
+| TimeGenerated | The UTC time stamp of the time the log is generated |
+| OperationName | The operation associated with the log record |
+| InstanceId | The ID of the instance that generates the log record |
+| DeploymentName | The name of the deployment associated with the log record |
+| Name | The name of the event |
+| Message | The content of the event |

articles/machine-learning/includes/endpoint-monitor-event-reference.md

@@ -2,31 +2,31 @@
 author: Blackmist
 ms.service: azure-machine-learning
 ms.topic: include
-ms.date: 04/22/2023
+ms.date: 01/02/2025
 ms.author: larryfr
 ---
 
 | Property | Description |
 |:--- |:--- |
-| Method | The requested method from client. 
-| Path | The requested path from client. 
-| SubscriptionId | The machine learning subscription ID of the online endpoint. 
-| AzureMLWorkspaceId | The machine learning workspace ID of the online endpoint. 
-| AzureMLWorkspaceName | The machine learning workspace name of the online endpoint. 
-| EndpointName | The name of the online endpoint. 
-| DeploymentName | The name of the online deployment. 
-| Protocol | The protocol of the request. 
-| ResponseCode | The final response code returned to the client. 
-| ResponseCodeReason | The final response code reason returned to the client. 
-| ModelStatusCode | The response status code from model. 
-| ModelStatusReason | The response status reason from model. 
-| RequestPayloadSize | The total bytes received from the client. 
-| ResponsePayloadSize | The total bytes sent back to the client. 
-| UserAgent | The user-agent header of the request, including comments but truncated to a max of 70 characters.
-| XRequestId | The request ID generated by Azure Machine Learning for internal tracing. 
-| XMSClientRequestId | The tracking ID generated by the client. 
-| TotalDurationMs | Duration in milliseconds from the request start time to the last response byte sent back to the client. If the client disconnected, it measures from the start time to client disconnect time. 
-| RequestDurationMs | Duration in milliseconds from the request start time to the last byte of the request received from the client. 
-| ResponseDurationMs | Duration in milliseconds from the request start time to the first response byte read from the model. 
-| RequestThrottlingDelayMs | Delay in milliseconds in request data transfer due to network throttling. 
-| ResponseThrottlingDelayMs | Delay in milliseconds in response data transfer due to network throttling. 
+| Method | The method that the client requests. |
+| Path | The path that the client requests. |
+| SubscriptionId | The machine learning subscription ID of the online endpoint. |
+| AzureMLWorkspaceId | The machine learning workspace ID of the online endpoint. |
+| AzureMLWorkspaceName | The machine learning workspace name of the online endpoint. |
+| EndpointName | The name of the online endpoint. |
+| DeploymentName | The name of the online deployment. |
+| Protocol | The protocol of the request. |
+| ResponseCode | The final response code that's returned to the client. |
+| ResponseCodeReason | The final response code reason that's returned to the client. |
+| ModelStatusCode | The response status code from the model. |
+| ModelStatusReason | The response status reason from the model. |
+| RequestPayloadSize | The total bytes received from the client. |
+| ResponsePayloadSize | The total bytes sent back to the client. |
+| UserAgent | The user-agent header of the request, including comments but truncated to a maximum of 70 characters. |
+| XRequestId | The request ID that Azure Machine Learning generates for internal tracing. |
+| XMSClientRequestId | The tracking ID that the client generates. |
+| TotalDurationMs | The duration in milliseconds from the request start time to the time the last response byte is sent back to the client. If the client disconnects, the duration is taken from the start time to the client disconnect time. |
+| RequestDurationMs | The duration in milliseconds from the request start time to the time the last byte of the request is received from the client. |
+| ResponseDurationMs | The duration in milliseconds from the request start time to the time the first response byte is read from the model. |
+| RequestThrottlingDelayMs | The delay in milliseconds in the request data transfer due to network throttling. |
+| ResponseThrottlingDelayMs | The delay in milliseconds in the response data transfer due to network throttling. |