Create and manage Azure Machine Learning workspaces

In this article, you'll create, view, and delete Azure Machine Learning workspaces for Azure Machine Learning, using the Azure portal or the SDK for Python

As your needs change or requirements for automation increase you can also create and delete workspaces using the CLI, or via the VS Code extension.


Create a workspace

This first example requires only minimal specification, and all dependent resources as well as the resource group will be created automatically.

from azureml.core import Workspace
   ws = Workspace.create(name='myworkspace',

Set create_resource_group to False if you have an existing Azure resource group that you want to use for the workspace.

You can also create a workspace that uses existing Azure resources with the Azure resource ID format. Find the specific Azure resource IDs in the Azure portal or with the SDK. This example assumes that the resource group, storage account, key vault, App Insights and container registry already exist.

import os
   from azureml.core import Workspace
   from azureml.core.authentication import ServicePrincipalAuthentication

   service_principal_password = os.environ.get("AZUREML_PASSWORD")

   service_principal_auth = ServicePrincipalAuthentication(

   ws = Workspace.create(name='myworkspace',
                         friendly_name='My workspace',

For more information, see Workspace SDK reference



For more information on using a private endpoint and virtual network with your workspace, see Network isolation and privacy.

The Azure Machine Learning Python SDK provides the PrivateEndpointConfig class, which can be used with Workspace.create() to create a workspace with a private endpoint. This class requires an existing virtual network.


Using a private endpoint with Azure Machine Learning workspace is currently in public preview. This preview is provided without a service level agreement, and it's not recommended for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews.

Multiple workspaces with private endpoint

When you create a private endpoint, a new Private DNS Zone named is created. This contains a link to the virtual network. If you create multiple workspaces with private endpoints in the same resource group, only the virtual network for the first private endpoint may be added to the DNS zone. To add entries for the virtual networks used by the additional workspaces/private endpoints, use the following steps:

  1. In the Azure portal, select the resource group that contains the workspace. Then select the Private DNS Zone resource named
  2. In the Settings, select Virtual network links.
  3. Select Add. From the Add virtual network link page, provide a unique Link name, and then select the Virtual network to be added. Select OK to add the network link.

For more information, see Azure Private Endpoint DNS configuration.

Vulnerability scanning

Azure Security Center provides unified security management and advanced threat protection across hybrid cloud workloads. You should allow Azure Security Center to scan your resources and follow its recommendations. For more, see Azure Container Registry image scanning by Security Center and Azure Kubernetes Services integration with Security Center.


By default, metrics and metadata for the workspace is stored in an Azure Cosmos DB instance that Microsoft maintains. This data is encrypted using Microsoft-managed keys.

To limit the data that Microsoft collects on your workspace, select High business impact workspace in the portal, or set hbi_workspace=true in Python. For more information on this setting, see Encryption at rest.


Selecting high business impact can only be done when creating a workspace. You cannot change this setting after workspace creation.

Use your own key

You can provide your own key for data encryption. Doing so creates the Azure Cosmos DB instance that stores metrics and metadata in your Azure subscription. Use the following steps to provide your own key:


Before following these steps, you must first perform the following actions:

  1. Authorize the Machine Learning App (in Identity and Access Management) with contributor permissions on your subscription.

  2. Follow the steps in Configure customer-managed keys to:

    • Register the Azure Cosmos DB provider
    • Create and configure an Azure Key Vault
    • Generate a key

    You do not need to manually create the Azure Cosmos DB instance, one will be created for you during workspace creation. This Azure Cosmos DB instance will be created in a separate resource group using a name based on this pattern: <your-workspace-resource-name>_<GUID>.

You cannot change this setting after workspace creation. If you delete the Azure Cosmos DB used by your workspace, you must also delete the workspace that is using it.

Use cmk_keyvault and resource_cmk_uri to specify the customer managed key.

from azureml.core import Workspace
   ws = Workspace.create(name='myworkspace',

Download a configuration file

If you will be creating a compute instance, skip this step. The compute instance has already created a copy of this file for you.

If you plan to use code on your local environment that references this workspace (ws), write the configuration file:


Place the file into the directory structure with your Python scripts or Jupyter Notebooks. It can be in the same directory, a subdirectory named .azureml, or in a parent directory. When you create a compute instance, this file is added to the correct directory on the VM for you.

Find a workspace

See a list of all the workspaces you can use.

Find your subscriptions in the Subscriptions page in the Azure portal. Copy the ID and use it in the code below to see all workspaces available for that subscription.

from azureml.core import Workspace


Delete a workspace

When you no longer need a workspace, delete it.

Delete the workspace ws:

ws.delete(delete_dependent_resources=False, no_wait=False)

The default action is not to delete resources associated with the workspace, i.e., container registry, storage account, key vault, and application insights. Set delete_dependent_resources to True to delete these resources as well.

Clean up resources


The resources that you created can be used as prerequisites to other Azure Machine Learning tutorials and how-to articles.

If you don't plan to use the resources that you created, delete them so you don't incur any charges:

  1. In the Azure portal, select Resource groups on the far left.

  2. From the list, select the resource group that you created.

  3. Select Delete resource group.

    Screenshot of the selections to delete a resource group in the Azure portal.

  4. Enter the resource group name. Then select Delete.


Resource provider errors

When creating an Azure Machine Learning workspace, or a resource used by the workspace, you may receive an error similar to the following messages:

  • No registered resource provider found for location {location}
  • The subscription is not registered to use namespace {resource-provider-namespace}

Most resource providers are automatically registered, but not all. If you receive this message, you need to register the provider mentioned.

For information on registering resource providers, see Resolve errors for resource provider registration.

Moving the workspace


Moving your Azure Machine Learning workspace to a different subscription, or moving the owning subscription to a new tenant, is not supported. Doing so may cause errors.

Deleting the Azure Container Registry

The Azure Machine Learning workspace uses Azure Container Registry (ACR) for some operations. It will automatically create an ACR instance when it first needs one.


Once an Azure Container Registry has been created for a workspace, do not delete it. Doing so will break your Azure Machine Learning workspace.


Examples of creating a workspace:

Next steps

Once you have a workspace, learn how to Train and deploy a model.