updates for ssh command and managed vnets

Author: Blackmist

articles/machine-learning/how-to-access-terminal.md

@@ -45,7 +45,7 @@ In addition to the steps above, you can also access the terminal from:
 * RStudio or Posit Workbench (formerly RStudio Workbench) (See [Add custom applications such as RStudio or Posit Workbench)](how-to-create-compute-instance.md?tabs=python#add-custom-applications-such-as-rstudio-or-posit-workbench)): Select the **Terminal** tab on top left.
 * Jupyter Lab:  Select the **Terminal** tile under the **Other** heading in the Launcher tab.
 * Jupyter:  Select **New>Terminal** on top right in the Files tab.
-* SSH to the machine, if you enabled SSH access when the compute instance was created.
+* SSH to the machine, if you enabled SSH access when the compute instance was created. If the compute instance is in a managed virtual network and doesn't have a public IP address, use the `az ml compute connect-ssh` command to connect to the compute instance.
 
 ## Copy and paste in the terminal
 

articles/machine-learning/how-to-manage-compute-instance.md

@@ -145,6 +145,10 @@ For each compute instance in a workspace that you created (or that was created f
 
 * Access Jupyter, JupyterLab, RStudio on the compute instance.
 * SSH into compute instance. SSH access is disabled by default but can be enabled at compute instance creation time. SSH access is through public/private key mechanism. The tab gives you details for SSH connection such as IP address, username, and port number. In a virtual network deployment, disabling SSH prevents SSH access from public internet. You can still SSH from within virtual network using private IP address of compute instance node and port 22.
+
+    > [!TIP]
+    > If the compute instances is in a *managed* virtual network and the public IP address is disabled, use the `az ml compute connect-ssh` command to connect to the compute instance.
+
 * Select the compute name to:
     * View details about a specific compute instance such as IP address, and region.
     * Create or modify the schedule for starting and stopping the compute instance. Scroll down to the bottom of the page to edit the schedule.

articles/machine-learning/how-to-managed-network-compute.md

@@ -158,6 +158,7 @@ You can't create a compute cluster or compute instance from the Azure portal. In
 ## Limitations
 
 * Creating a compute cluster in a different region than the workspace isn't supported when using a managed virtual network.
+* If the compute is in a managed network and also configured for no public IP, use the `az ml compute connect-ssh` command to connect to the compute instance over SSH.
 
 ### Migration of compute resources
 

articles/machine-learning/how-to-managed-network.md

@@ -1103,7 +1103,7 @@ The Azure Machine Learning managed VNet feature is free. However, you're charged
 * Creating a compute cluster in a different region than the workspace isn't supported when using a managed VNet.
 * Kubernetes and attached VMs aren't supported in an Azure Machine Learning managed VNet.
 * Using FQDN outbound rules increases the cost of the managed VNet because FQDN rules use Azure Firewall. For more information, see [Pricing](#pricing).
-* If your compute instance is in a managed network and is also configured for no public IP, use the `az ml compute connect-ssh` command to connect to it using SSH.
+* If your compute instance is in a managed network and is configured for no public IP, use the `az ml compute connect-ssh` command to connect to it using SSH.
 
 ### Migration of compute resources