As the requirements of your machine learning application change, you might need to move your workspace to a different Azure subscription. For example, you might need to move the workspace in the following situations:
Promote workspace from test subscription to production subscription.
Change the design and architecture of your application.
Move workspace to a subscription with more available quota.
Move workspace to a subscription with different cost center.
Moving the workspace enables you to migrate the workspace and its contents as a single, automated step. The following table describes the workspace contents that are moved:
Workspace contents
Moved with workspace
Datastores
Yes
Datasets
No
Experiment jobs
Yes
Environments
Yes
Models and other assets stored in the workspace
Yes
Compute resources
No
Endpoints
No
Important
Workspace move 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.
Prerequisites
An Azure Machine Learning workspace in the source subscription. For more information, see Create workspace resources.
You must have permissions to manage resources in both source and target subscriptions. For example, Contributor or Owner role at the subscription level. For more information on roles, see Azure roles.
You need permissions to delete resources from the source location.
You need permissions to create resources in the destination location.
Thee move mustn't violate Azure Policies in the destination location.
Any role assignments to the source workspace scope aren't moved; you must recreate them in the destination.
The destination subscription must be registered for required resource providers. The following table contains a list of the resource providers required by Azure Machine Learning:
Resource provider
Why it's needed
Microsoft.MachineLearningServices
Creating the Azure Machine Learning workspace.
Microsoft.Storage
Azure Storage Account is used as the default storage for the workspace.
Microsoft.ContainerRegistry
Azure Container Registry is used by the workspace to build Docker images.
Microsoft.KeyVault
Azure Key Vault is used by the workspace to store secrets.
Microsoft.Notebooks/NotebookProxies
Integrated notebooks on Azure Machine Learning compute instance.
Microsoft.ContainerService
If you plan on deploying trained models to Azure Kubernetes Services.
If you plan on using a customer-managed key with Azure Machine Learning, then the following service providers must be registered:
Resource provider
Why it's needed
Microsoft.DocumentDB/databaseAccounts
Azure Cosmos DB instance that logs metadata for the workspace.
Microsoft.Search/searchServices
Azure Search provides indexing capabilities for the workspace.
The workspace must be quiescent before the move; computes are deleted, no live endpoints or running experiments.
Moving a workspace that has private endpoints configured is supported. The private endpoints are disconnected and transitive private endpoints are recreated after the move. However, you're responsible for approving the new private endpoints (including the workspace private endpoint) after the move.
Limitations
Workspace move isn't meant for replicating workspaces, or moving individual assets such as models or datasets from one workspace to another.
Workspace move doesn't support migration across Azure regions.
Workspace move doesn't support migration across Microsoft Entra tenants.
The workspace mustn't be in use during the move operation. Verify that all experiment jobs, data profiling jobs, and labeling projects have completed. Also verify that inference endpoints aren't being invoked.
The workspace becomes unavailable during the move.
Before to the move, you must delete or detach computes and inference endpoints from the workspace.
Workspace with currently running labeling projects.
Workspace linked with Azure Databricks.
Workspace move across regions.
Prepare and validate the move
In Azure CLI, set the subscription to that of your origin workspace
az account set -s origin-sub-id
Verify that the origin workspace isn't being used. Check that any experiment jobs, data profiling jobs, or labeling projects have completed. Also verify that inferencing endpoints aren't being invoked.
Delete or detach any computes from the workspace, and delete any inferencing endpoints. Moving computes and endpoints isn't supported. Also note that the workspace becomes unavailable during the move.
Create a destination resource group in the new subscription. This resource group will contain the workspace after the move. The destination must be in the same region as the origin.
az group create -g destination-rg -l my-region --subscription destination-sub-id
The following command demonstrates how to validate the move operation for workspace. You can include associated resources such as storage account, container registry, key vault, and application insights into the move by adding them to the resources list. The validation may take several minutes. In this command, origin-rg is the origin resource group, while destination-rg is the destination. The subscription IDs are origin-sub-id and destination-sub-id, while the workspace is origin-workspace-name:
Once the validation has succeeded, move the workspace. You may also include any associated resources into move operation by adding them to the ids parameter. This operation may take several minutes.
az resource move --destination-group destination-rg --destination-subscription-id destination-sub-id --ids "/subscriptions/origin-sub-id/resourceGroups/origin-rg/providers/Microsoft.MachineLearningServices/workspaces/origin-workspace-name"
After the move has completed, recreate any computes and redeploy any web service endpoints at the new location.
Scenario: Move a workspace with nondefault datastores
The automated workspace move operation doesn't move nondefault datastores. Use the following steps to manually update the data store credentials after the move.
Within Azure Machine Learning studio, select Data and then select a nondefault data store. For each nondefault data store, check if the Subscription ID and Resource group name fields are empty. If they are, select Update authentication.
In the Update datastore credentials dialog, select the subscription ID and resource group name that the storage account was moved to and then select Save.
If the Subscription ID and Resource group name fields are populated for the nondefault data assets, and refer to the subscription ID and resource group prior to the move, use the following steps:
Navigate to the Datastores tab, select the datastore, and then select Unregister.
Select Create to create a new datastore.
From the Create datastore dialog, use the same name, type, etc. as the datastore you unregistered. Select the subscription ID and storage account from the new location. Finally, select Create to create the new datastore registration.
The workspace is the top-level resource for Azure Machine Learning. It keeps a history of all training runs, with logs, metrics, output, and a snapshot of your scripts.
Explore and configure the Azure Machine Learning workspace, its resources and its assets. Explore which developer tools you can use to interact with the workspace, focusing on the CLI and Python SDK v2.
Manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Python, Azure Machine Learning and MLflow.