Manage access to an Azure Machine Learning workspace

In this article, you learn how to manage access to an Azure Machine Learning workspace. Role-based access control (RBAC) is used to manage access to Azure resources. Users in your Azure Active Directory are assigned specific roles, which grant access to resources. Azure provides both built-in roles and the ability to create custom roles.

Default roles

An Azure Machine Learning workspace is an Azure resource. Like other Azure resources, when a new Azure Machine Learning workspace is created, it comes with three default roles. You can add users to the workspace and assign them to one of these built-in roles.

Role Access level
Reader Read-only actions in the workspace. Readers can list and view assets in a workspace, but can't create or update these assets.
Contributor View, create, edit, or delete (where applicable) assets in a workspace. For example, contributors can create an experiment, create or attach a compute cluster, submit a run, and deploy a web service.
Owner Full access to the workspace, including the ability to view, create, edit, or delete (where applicable) assets in a workspace. Additionally, you can change role assignments.


Role access can be scoped to multiple levels in Azure. For example, someone with owner access to a workspace may not have owner access to the resource group that contains the workspace. For more information, see How RBAC works.

For more information on specific built-in roles, see Built-in roles for Azure.

Manage workspace access

If you're an owner of a workspace, you can add and remove roles for the workspace. You can also assign roles to users. Use the following links to discover how to manage access:

If you have installed the Azure Machine Learning CLI, you can also use a CLI command to assign roles to users.

az ml workspace share -n <workspace_name> -g <resource_group_name> --role <role_name> --user <user_corp_email_address>

The user field is the email address of an existing user in the instance of Azure Active Directory where the workspace parent subscription lives. Here is an example of how to use this command:

az ml workspace share -n my_workspace -g my_resource_group --role Contributor --user

Create custom role

If the built-in roles are insufficient, you can create custom roles. Custom roles might have read, write, delete, and compute resource permissions in that workspace. You can make the role available at a specific workspace level, a specific resource group level, or a specific subscription level.


You must be an owner of the resource at that level to create custom roles within that resource.

To create a custom role, first construct a role definition JSON file that specifies the permission and scope for the role. The following example defines a custom role named "Data Scientist" scoped at a specific workspace level:

data_scientist_role.json :

    "Name": "Data Scientist",
    "IsCustom": true,
    "Description": "Can run experiment but can't create or delete compute.",
    "Actions": ["*"],
    "NotActions": [
    "AssignableScopes": [

You can change the AssignableScopes field to set the scope of this custom role at the subscription level, the resource group level, or a specific workspace level.

This custom role can do everything in the workspace except for the following actions:

  • It can't create or update a compute resource.
  • It can't delete a compute resource.
  • It can't add, delete, or alter role assignments.
  • It can't delete the workspace.

To deploy this custom role, use the following Azure CLI command:

az role definition create --role-definition data_scientist_role.json

After deployment, this role becomes available in the specified workspace. Now you can add and assign this role in the Azure portal. Or, you can assign this role to a user by using the az ml workspace share CLI command:

az ml workspace share -n my_workspace -g my_resource_group --role "Data Scientist" --user

For more information, see Custom roles for Azure resources.

Next steps