Address PR warnings; update link text

JKirsch1 · JKirsch1 · commit 197bdc3c1c26 · 2025-01-02T10:06:34.000-05:00
diff --git a/articles/machine-learning/how-to-authenticate-online-endpoint.md b/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.
 
diff --git a/articles/machine-learning/how-to-deploy-online-endpoints.md b/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)] -->
 
diff --git a/articles/machine-learning/how-to-manage-quotas.md b/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).
 
diff --git a/articles/machine-learning/how-to-monitor-online-endpoints.md b/articles/machine-learning/how-to-monitor-online-endpoints.md
@@ -76,7 +76,7 @@ Another way to view the metrics page for an endpoint or deployment is to go dire
 
    Online endpoints and deployments are Azure Resource Manager resources. You can find them by going to their resource group and then looking for the resource types **Machine Learning online endpoint** and **Machine Learning online deployment**.
 
-1. Under **Monitoring**, select **Metrics**.
+1. On the resource page, under **Monitoring**, select **Metrics**.
 
    :::image type="content" source="media/how-to-monitor-online-endpoints/endpoint-metrics-azure-portal.png" alt-text="Screenshot of the Azure portal that shows the Metrics page for a deployment. Monitoring and Metrics are highlighted." lightbox="media/how-to-monitor-online-endpoints/endpoint-metrics-azure-portal.png":::
 
@@ -136,7 +136,7 @@ The options for code are the Azure Machine Learning CLI and the Azure Machine Le
 - 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).
 
-For more information, see [Autoscaling online endpoints](how-to-autoscale-endpoints.md).
+For more information, see [Autoscale online endpoints in Azure Machine Learning](how-to-autoscale-endpoints.md).
 
 ## Use logs
 
@@ -173,7 +173,7 @@ There are three logs that you can turn on for online endpoints:
 ### Turn on logs
 
 > [!IMPORTANT]
-> Logging uses the Log Analytics feature of Monitor. If you don't currently have a Log Analytics workspace, you can create one by following the steps in [Create a Log Analytics workspace in the Azure portal](/azure/azure-monitor/logs/quick-create-workspace#create-a-workspace).
+> Logging uses the Log Analytics feature of Monitor. If you don't currently have a Log Analytics workspace, you can create one by following the steps in [Create a workspace](/azure/azure-monitor/logs/quick-create-workspace#create-a-workspace).
 
 1. In the [Azure portal](https://portal.azure.com), go to the resource group that contains your endpoint, and then select the endpoint.
 
diff --git a/articles/machine-learning/how-to-safely-rollout-online-endpoints.md b/articles/machine-learning/how-to-safely-rollout-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:
 
diff --git a/articles/machine-learning/prompt-flow/how-to-deploy-for-real-time-inference.md b/articles/machine-learning/prompt-flow/how-to-deploy-for-real-time-inference.md
@@ -288,7 +288,7 @@ You can view various metrics (request numbers, request latency, network bytes, C
 
 :::image type="content" source="./media/how-to-deploy-for-real-time-inference/view-metrics.png" alt-text="Screenshot of the endpoint detail page with view metrics highlighted. " lightbox = "./media/how-to-deploy-for-real-time-inference/view-metrics.png":::
 
-For more information on how to view online endpoint metrics, see [Monitor online endpoints](../how-to-monitor-online-endpoints.md#metrics).
+For more information on how to view online endpoint metrics, see [Monitor online endpoints](../how-to-monitor-online-endpoints.md#use-metrics).
 
 ### View prompt flow endpoints specific metrics and tracing data (optional)
 
diff --git a/articles/machine-learning/prompt-flow/how-to-deploy-to-code.md b/articles/machine-learning/prompt-flow/how-to-deploy-to-code.md
@@ -464,7 +464,7 @@ While tuning above parameters, you need to monitor the following metrics to ensu
 
 #### Collect general metrics
 
-You can view [general metrics of online deployment (request numbers, request latency, network bytes, CPU/GPU/Disk/Memory utilization, and more)](../how-to-monitor-online-endpoints.md#metrics).
+You can view [general metrics of online deployment (request numbers, request latency, network bytes, CPU/GPU/Disk/Memory utilization, and more)](../how-to-monitor-online-endpoints.md#use-metrics).
 
 #### Collect tracing data and system metrics during inference time
 
diff --git a/articles/machine-learning/tutorial-deploy-model.md b/articles/machine-learning/tutorial-deploy-model.md
@@ -451,7 +451,7 @@ If you open the metrics for the online endpoint, you can set up the page to see
 
 :::image type="content" source="media/tutorial-deploy-model/view-endpoint-metrics-in-azure-portal.png" alt-text="Screenshot showing online endpoint metrics in the Azure portal." lightbox="media/tutorial-deploy-model/view-endpoint-metrics-in-azure-portal.png":::
 
-For more information on how to view online endpoint metrics, see [Monitor online endpoints](how-to-monitor-online-endpoints.md#metrics).
+For more information on how to view online endpoint metrics, see [Monitor online endpoints](how-to-monitor-online-endpoints.md#use-metrics).
 
 ## Send all traffic to the new deployment
 Once you're fully satisfied with your `green` deployment, switch all traffic to it.
