Create and manage Azure Machine Learning workspaces in the Azure portal

APPLIES TO: yesBasic edition yesEnterprise edition                    (Upgrade to Enterprise edition)

In this article, you'll create, view, and delete Azure Machine Learning workspaces in the Azure portal for Azure Machine Learning. The portal is the easiest way to get started with workspaces but as your needs change or requirements for automation increase you can also create and delete workspaces using the CLI, with Python code or via the VS Code extension.

Create a workspace

To create a workspace, you need an Azure subscription. If you don't have an Azure subscription, create a free account before you begin. Try the free or paid version of Azure Machine Learning today.

  1. Sign in to the Azure portal by using the credentials for your Azure subscription.

  2. In the upper-left corner of Azure portal, select + Create a resource.

    Create a new resource

  3. Use the search bar to find Machine Learning.

  4. Select Machine Learning.

  5. In the Machine Learning pane, select Create to begin.

  6. Provide the following information to configure your new workspace:

    Field Description
    Workspace name Enter a unique name that identifies your workspace. In this example, we use docs-ws. Names must be unique across the resource group. Use a name that's easy to recall and to differentiate from workspaces created by others. The workspace name is case-insensitive.
    Subscription Select the Azure subscription that you want to use.
    Resource group Use an existing resource group in your subscription or enter a name to create a new resource group. A resource group holds related resources for an Azure solution. In this example, we use docs-aml.
    Location Select the location closest to your users and the data resources to create your workspace.
    Workspace edition Select Basic or Enterprise. This workspace edition determines the features to which you'll have access and pricing. Learn more about Basic and Enterprise edition offerings.

    Configure your workspace

  7. When you're finished configuring the workspace, select Review + Create.

  8. Review the settings and make any additional changes or corrections. When you're satisfied with the settings, select Create.


    It can take several minutes to create your workspace in the cloud.

    When the process is finished, a deployment success message appears.

  9. To view the new workspace, select Go to resource.

Download a configuration file

  1. If you will be creating a compute instance, skip this step.

  2. If you plan to use code on your local environment that references this workspace, select Download config.json from the Overview section of the workspace.

    Download config.json

    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.

Upgrade to Enterprise edition

You can upgrade your workspace from Basic edition to Enterprise edition to take advantage of the enhanced features such as low-code experiences and enhanced security features.

  1. Sign in to Azure Machine Learning studio.

  2. Select the workspace that you wish to upgrade.

  3. Select Learn more at the top right of the page.

    Upgrade a workspace

  4. Select Upgrade in the window that appears.


You cannot downgrade an Enterprise edition workspace to a Basic edition workspace.

Find a workspace

  1. Sign in to the Azure portal.

  2. In the top search field, type Machine Learning.

  3. Select Machine Learning.

    Search for Azure Machine Learning workspace

  4. Look through the list of workspaces found. You can filter based on subscription, resource groups, and locations.

  5. Select a workspace to display its properties.

Delete a workspace

In the Azure portal, select Delete at the top of the workspace you wish to delete.

Delete workspace

Clean up resources


The resources 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 you created, delete them, so you don't incur any charges:

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

    Delete in the Azure portal

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

  3. Select Delete resource group.

  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.

Next steps

Follow the full-length tutorial to learn how to use a workspace to build, train, and deploy models with Azure Machine Learning.