updates per PM feedback

Author: Larry Franks

articles/machine-learning/concept-endpoints.md

@@ -146,7 +146,7 @@ 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) | Unsupported                                                                                                             |
 | **Application Insights**      | Supported                                                                                                                         | Supported                                                                                                               |
 | **Managed identity**          | [Supported](how-to-access-resources-from-endpoints-managed-identities.md)                                                         | Supported                                                                                                               |
-| **Virtual Network (VNET)**    | [Supported](how-to-secure-online-endpoint.md) (preview)                                                                           | Supported                                                                                                               |
+| **Virtual Network (VNET)**    | [Supported](how-to-secure-online-endpoint.md)                                                                           | Supported                                                                                                               |
 | **View costs**                | [Endpoint and deployment level](how-to-view-online-endpoints-costs.md)                                                            | Cluster level                                                                                                           |
 | **Mirrored traffic**          | [Supported](how-to-safely-rollout-online-endpoints.md#test-the-deployment-with-mirrored-traffic-preview)                          | Unsupported                                                                                                             |
 | **No-code deployment**        | Supported ([MLflow](how-to-deploy-mlflow-models-online-endpoints.md) and [Triton](how-to-deploy-with-triton.md) models)           | Supported ([MLflow](how-to-deploy-mlflow-models-online-endpoints.md) and [Triton](how-to-deploy-with-triton.md) models) |

articles/machine-learning/how-to-prevent-data-loss-exfiltration.md

@@ -104,9 +104,6 @@ __Allow__ outbound traffic over __TCP port 443__ to the following FQDNs. Replace
 
 * `<region>.batch.azure.com`
 * `<region>.service.batch.com`
-* `*.blob.core.windows.net` - A Service Endpoint Policy will be applied in a later step to limit outbound traffic. 
-* `*.queue.core.windows.net` - A Service Endpoint Policy will be applied in a later step to limit outbound traffic. 
-* `*.table.core.windows.net` - A Service Endpoint Policy will be applied in a later step to limit outbound traffic. 
 
 > [!IMPORTANT]
 > If you use one firewall for multiple Azure services, having outbound storage rules impacts other services. In this case, limit thee source IP of the outbound storage rule to the address space of the subnet that contains your compute instance and compute cluster resources. This limits the rule to the compute resources in the subnet.

articles/machine-learning/how-to-secure-training-vnet.md

@@ -147,7 +147,11 @@ In the `az ml compute create` command, replace the following values:
 * `AmlCompute` or `ComputeInstance`: Specifying `AmlCompute` creates a *compute cluster*. `ComputeInstance` creates a *compute instance*.
 
 ```azurecli
-az ml compute create --resource-group rg --workspace-name ws --vnet-name yourvnet --subnet yoursubnet --type AmlCompute or ComputeInstance --enable-node-public-ip false
+# create a compute cluster with no public IP
+az ml compute create --name cpu-cluster --resource-group rg --workspace-name ws --vnet-name yourvnet --subnet yoursubnet --type AmlCompute --set enable-node-public-ip=False
+
+# create a compute instance with no public IP
+az ml compute create --name myci --resource-group rg --workspace-name ws --vnet-name yourvnet --subnet yoursubnet --type ComputeInstance --set enable-node-public-ip=False
 ```
 
 # [Python](#tab/python)
@@ -246,7 +250,11 @@ In the `az ml compute create` command, replace the following values:
 * `AmlCompute` or `ComputeInstance`: Specifying `AmlCompute` creates a *compute cluster*. `ComputeInstance` creates a *compute instance*.
 
 ```azurecli
-az ml compute create --resource-group rg --workspace-name ws --vnet-name yourvnet --subnet yoursubnet --type AmlCompute or ComputeInstance
+# create a compute cluster with a public IP
+az ml compute create --name cpu-cluster --resource-group rg --workspace-name ws --vnet-name yourvnet --subnet yoursubnet --type AmlCompute
+
+# create a compute instance with a public IP
+az ml compute create --name myci --resource-group rg --workspace-name ws --vnet-name yourvnet --subnet yoursubnet --type ComputeInstance
 ```
 
 # [Python](#tab/python)