fixing merge blockers

Author: Blackmist

articles/machine-learning/concept-endpoints.md

@@ -143,7 +143,7 @@ The following table highlights the key differences between managed online endpoi
 | **Out-of-box logging** | [Azure Logs and Log Analytics at endpoint level](how-to-deploy-managed-online-endpoints.md#optional-integrate-with-log-analytics) | Supported |
 | **Application Insights** | Supported | Supported |
 | **Managed identity** | [Supported](how-to-access-resources-from-endpoints-managed-identities.md) | Supported |
-| **Virtual Network (VNET)** | [Supported]() (preview) | Supported |
+| **Virtual Network (VNET)** | [Supported](how-to-secure-online-endpoint.md) (preview) | Supported |
 | **View costs** | [Endpoint and deployment level](how-to-view-online-endpoints-costs.md) | Cluster level |
 | **Mirrored traffic** | [Supported](how-to-safely-rollout-managed-endpoints.md#test-the-deployment-with-mirrored-traffic-preview) | Unsupported
 

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

@@ -53,19 +53,18 @@ Depending on the resource that you select, the metrics that you see will be diff
 - Active connection count
 - Network bytes
 
-<a id="bandwidth-throttling"></a>
-
-> [!NOTE]
-> Bandwidth will be throttled if the limits are exceeded (see managed online endpoints section in [Manage and increase quotas for resources with Azure Machine Learning](how-to-manage-quotas.md#azure-machine-learning-managed-online-endpoints)). To determine if requests are throttled:
-> - Monitor the "Network bytes" metric
-> - The response trailers will have the fields: `ms-azureml-bandwidth-request-delay-ms` and `ms-azureml-bandwidth-response-delay-ms`. The values of the fields are the delays, in milliseconds, of the bandwidth throttling.
-
 Split on the following dimensions:
 
 - Deployment
 - Status Code
 - Status Code Class
 
+#### Bandwidth throttling
+
+Bandwidth will be throttled if the limits are exceeded (see managed online endpoints section in [Manage and increase quotas for resources with Azure Machine Learning](how-to-manage-quotas.md#azure-machine-learning-managed-online-endpoints)). To determine if requests are throttled:
+- Monitor the "Network bytes" metric
+- The response trailers will have the fields: `ms-azureml-bandwidth-request-delay-ms` and `ms-azureml-bandwidth-response-delay-ms`. The values of the fields are the delays, in milliseconds, of the bandwidth throttling.
+
 ### Metrics at deployment scope
 
 - CPU Utilization Percentage

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

@@ -77,8 +77,6 @@ The following are the resources that the deployment communicates with over the p
 
 When you configure the `egress_public_network_access` to `disabled`, a new private endpoint is created per deployment, per service. For example, if you set the flag to `true` for three deployments to an online endpoint, nine private endpoints are created. Each deployment would have three private endpoints that are used to communicate with the workspace, blob, and container registry.
 
-The following diagram 
-
 ```azurecli
 az ml online-deployment create -f deployment.yml --set egress_public_network_access=disabled
 ```