updated to include MSI CI support

Author: meyetman

articles/machine-learning/v1/how-to-setup-authentication.md

@@ -29,7 +29,7 @@ Learn how to set up authentication to your Azure Machine Learning workspace. Aut
 
 * __Azure CLI session__: You use an active Azure CLI session to authenticate. Azure CLI authentication is used during _experimentation and iterative development_, or when you need an _automated process to authenticate_ to the service using a pre-authenticated session. You can log in to Azure via the Azure CLI on your local workstation, without storing credentials in Python code or prompting the user to authenticate. Similarly, you can reuse the same scripts as part of continuous integration and deployment pipelines, while authenticating the Azure CLI with a service principal identity.
 
-* __Managed identity__: When using the Azure Machine Learning SDK _on an Azure Virtual Machine_, you can use a managed identity for Azure. This workflow allows the VM to connect to the workspace using the managed identity, without storing credentials in Python code or prompting the user to authenticate. Azure Machine Learning compute clusters can also be configured to use a managed identity to access the workspace when _training models_.
+* __Managed identity__: When using the Azure Machine Learning SDK _on an Azure Virtual Machine_, you can use a managed identity for Azure. This workflow allows the VM to connect to the workspace using the managed identity, without storing credentials in Python code or prompting the user to authenticate. Azure Machine Learning compute clusters and compute instances can also be configured to use a managed identity to access the workspace when _training models_.
 
 Regardless of the authentication workflow used, Azure role-based access control (Azure RBAC) is used to scope the level of access (authorization) allowed to the resources. For example, an admin or automation process might have access to create a compute instance, but not use it, while a data scientist could use it, but not delete or create it. For more information, see [Manage access to Azure Machine Learning workspace](../how-to-assign-roles.md).
 
@@ -129,7 +129,7 @@ The easiest way to create an SP and grant access to your workspace is by using t
 ## Configure a managed identity
 
 > [!IMPORTANT]
-> Managed identity is only supported when using the Azure Machine Learning SDK from an Azure Virtual Machine or with an Azure Machine Learning compute cluster.
+> Managed identity is only supported when using the Azure Machine Learning SDK from an Azure Virtual Machine or with an Azure Machine Learning compute cluster or compute instance.
 
 ### Managed identity with a VM
 
@@ -238,7 +238,7 @@ ws.get_details()
 
 ## Use managed identity authentication
 
-To authenticate to the workspace from a VM or compute cluster that is configured with a managed identity, use the `MsiAuthentication` class. The following example demonstrates how to use this class to authenticate to a workspace:
+To authenticate to the workspace from a VM, compute cluster, or compute instance that is configured with a managed identity, use the `MsiAuthentication` class. The following example demonstrates how to use this class to authenticate to a workspace:
 
 ```python
 from azureml.core.authentication import MsiAuthentication
@@ -261,4 +261,4 @@ can require two-factor authentication, or allow sign in only from managed device
 
 * [How to use secrets in training](../how-to-use-secrets-in-runs.md).
 * [How to configure authentication for models deployed as a web service](how-to-authenticate-web-service.md).
-* [Consume an Azure Machine Learning model deployed as a web service](how-to-consume-web-service.md).
+* [Consume an Azure Machine Learning model deployed as a web service](how-to-consume-web-service.md).