clarifying some questions from customers. Plus acrolinx

Author: Larry Franks

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

@@ -27,7 +27,7 @@ The following diagram shows how communications flow through private endpoints to
 
 ## Prerequisites
 
-* To use Azure machine learning, you must have an Azure subscription. If you don't have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://azure.microsoft.com/free/) today.
+* To use Azure Machine Learning, you must have an Azure subscription. If you don't have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://azure.microsoft.com/free/) today.
 
 * You must install and configure the Azure CLI and `ml` extension or the Azure Machine Learning Python SDK v2. For more information, see the following articles:
 
@@ -64,6 +64,10 @@ The following diagram shows how communications flow through private endpoints to
 
 * Secure outbound communication creates three private endpoints per deployment. One to the Azure Blob storage, one to the Azure Container Registry, and one to your workspace.
 
+    > [!IMPORTANT]
+    > * Each managed online endpoint deployment has it's own independent Azure Machine Learning managed VNet. If the endpoint has multiple deployments, each deployment has it's own managed VNet.
+    > * We do not support peering between a deployment's managed VNet and your client VNet. For secure access to resources needed by the deployment, we use private endpoints to communicate with the resources.
+
 * When you use network isolation with a deployment, Azure Log Analytics is partially supported. All metrics and the `AMLOnlineEndpointTrafficLog` table are supported via Azure Log Analytics. `AMLOnlineEndpointConsoleLog` and `AMLOnlineEndpointEventLog` tables are currently not supported. As a workaround, you can use the [az ml online-deployment get_logs](/cli/azure/ml/online-deployment#az-ml-online-deployment-get-logs) CLI command, the [OnlineDeploymentOperations.get_logs()](/python/api/azure-ai-ml/azure.ai.ml.operations.onlinedeploymentoperations#azure-ai-ml-operations-onlinedeploymentoperations-get-logs) Python SDK, or the Deployment log tab in the Azure Machine Learning studio instead. For more information, see [Monitoring online endpoints](how-to-monitor-online-endpoints.md).
 
 * You can configure public access to a __managed online endpoint__ (_inbound_ and _outbound_). You can also configure [public access to an Azure Machine Learning workspace](how-to-configure-private-link.md#enable-public-access).
@@ -216,7 +220,7 @@ The following diagram shows the overall architecture of this example:
 
 :::image type="content" source="./media/how-to-secure-online-endpoint/endpoint-network-isolation-diagram.png" alt-text="Diagram of the services created.":::
 
-To create the resources, use the following Azure CLI commands. To create a resource group. Replace `<my-resource-group>` and `<my-location>` with the desierd values.  
+To create the resources, use the following Azure CLI commands. To create a resource group. Replace `<my-resource-group>` and `<my-location>` with the desired values.  
 
 ```azurecli
 # create resource group