Manage access to an Azure Machine Learning workspace

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

Tip

While this article focuses on Azure Machine Learning, individual services that Azure ML relies on provide their own RBAC settings. For example, using the information in this article, you can configure who can submit scoring requests to a model deployed as a web service on Azure Kubernetes Service. But Azure Kubernetes Service provides its own set of Azure roles. For service specific RBAC information that may be useful with Azure Machine Learning, see the following links:

Warning

Applying some roles may limit UI functionality in Azure Machine Learning studio for other users. For example, if a user's role does not have the ability to create a compute instance, the option to create a compute instance will not be available in studio. This behavior is expected, and prevents the user from attempting operations that would return an access denied error.

Default roles

Azure Machine Learning workspaces have a four built-in roles that are available by default. When adding users to a workspace, they can be assigned one of the built-in roles described below.

Role Access level
AzureML Data Scientist Can perform all actions within an Azure Machine Learning workspace, except for creating or deleting compute resources and modifying the workspace itself.
Reader Read-only actions in the workspace. Readers can list and view assets, including datastore credentials, in a workspace. Readers 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.

Important

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 Azure RBAC works.

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:

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.

Note

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 Custom" scoped at a specific workspace level:

data_scientist_custom_role.json :

{
    "Name": "Data Scientist Custom",
    "IsCustom": true,
    "Description": "Can run experiment but can't create or delete compute.",
    "Actions": ["*"],
    "NotActions": [
        "Microsoft.MachineLearningServices/workspaces/*/delete",
        "Microsoft.MachineLearningServices/workspaces/write",
        "Microsoft.MachineLearningServices/workspaces/computes/*/write",
        "Microsoft.MachineLearningServices/workspaces/computes/*/delete", 
        "Microsoft.Authorization/*/write"
    ],
    "AssignableScopes": [
        "/subscriptions/<subscription_id>/resourceGroups/<resource_group_name>/providers/Microsoft.MachineLearningServices/workspaces/<workspace_name>"
    ]
}

Tip

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. The above custom role is just an example, see some suggested custom roles for the Azure Machine Learning service.

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.

For more information on custom roles, see Azure custom roles.

Azure Machine Learning operations

For more information on the operations (actions and not actions) usable with custom roles, see Resource provider operations. You can also use the following Azure CLI command to list operations:

az provider operation show –n Microsoft.MachineLearningServices

List custom roles

In the Azure CLI, run the following command:

az role definition list --subscription <sub-id> --custom-role-only true

To view the role definition for a specific custom role, use the following Azure CLI command. The <role-name> should be in the same format returned by the command above:

az role definition list -n <role-name> --subscription <sub-id>

Update a custom role

In the Azure CLI, run the following command:

az role definition update --role-definition update_def.json --subscription <sub-id>

You need to have permissions on the entire scope of your new role definition. For example if this new role has a scope across three subscriptions, you need to have permissions on all three subscriptions.

Note

Role updates can take 15 minutes to an hour to apply across all role assignments in that scope.

Use Azure Resource Manager templates for repeatability

If you anticipate that you will need to recreate complex role assignments, an Azure Resource Manager template can be a big help. The machine-learning-dependencies-role-assignment template shows how role assignments can be specified in source code for reuse.

Common scenarios

The following table is a summary of Azure Machine Learning activities and the permissions required to perform them at the least scope. For example, if an activity can be performed with a workspace scope (Column 4), then all higher scope with that permission will also work automatically:

Important

All paths in this table that start with / are relative paths to Microsoft.MachineLearningServices/ :

Activity Subscription-level scope Resource group-level scope Workspace-level scope
Create new workspace Not required Owner or contributor N/A (becomes Owner or inherits higher scope role after creation)
Request subscription level Amlcompute quota or set workspace level quota Owner, or contributor, or custom role
allowing /locations/updateQuotas/action
at subscription scope
Not Authorized Not Authorized
Create new compute cluster Not required Not required Owner, contributor, or custom role allowing: /workspaces/computes/write
Create new compute instance Not required Not required Owner, contributor, or custom role allowing: /workspaces/computes/write
Submitting any type of run Not required Not required Owner, contributor, or custom role allowing: "/workspaces/*/read", "/workspaces/environments/write", "/workspaces/experiments/runs/write", "/workspaces/metadata/artifacts/write", "/workspaces/metadata/snapshots/write", "/workspaces/environments/build/action", "/workspaces/experiments/runs/submit/action", "/workspaces/environments/readSecrets/action"
Publishing pipelines and endpoints Not required Not required Owner, contributor, or custom role allowing: "/workspaces/endpoints/pipelines/*", "/workspaces/pipelinedrafts/*", "/workspaces/modules/*"
Deploying a registered model on an AKS/ACI resource Not required Not required Owner, contributor, or custom role allowing: "/workspaces/services/aks/write", "/workspaces/services/aci/write"
Scoring against a deployed AKS endpoint Not required Not required Owner, contributor, or custom role allowing: "/workspaces/services/aks/score/action", "/workspaces/services/aks/listkeys/action" (when you are not using Azure Active Directory auth) OR "/workspaces/read" (when you are using token auth)
Accessing storage using interactive notebooks Not required Not required Owner, contributor, or custom role allowing: "/workspaces/computes/read", "/workspaces/notebooks/samples/read", "/workspaces/notebooks/storage/*", "/workspaces/listStorageAccountKeys/action"
Create new custom role Owner, contributor, or custom role allowing Microsoft.Authorization/roleDefinitions/write Not required Owner, contributor, or custom role allowing: /workspaces/computes/write

Tip

If you receive a failure when trying to create a workspace for the first time, make sure that your role allows Microsoft.MachineLearningServices/register/action. This action allows you to register the Azure Machine Learning resource provider with your Azure subscription.

User-assigned managed identity with Azure ML compute cluster

To assign a user assigned identity to an Azure Machine Learning compute cluster, you need write permissions to create the compute and the Managed Identity Operator Role. For more information on Azure RBAC with Managed Identities, read How to manage user assigned identity

MLflow operations

To perform MLflow operations with your Azure Machine Learning workspace, use the following scopes your custom role:

MLflow operation Scope
List all experiments in the workspace tracking store, get an experiment by id, get an experiment by name Microsoft.MachineLearningServices/workspaces/experiments/read
Create an experiment with a name , set a tag on an experiment, restore an experiment marked for deletion Microsoft.MachineLearningServices/workspaces/experiments/write
Delete an experiment Microsoft.MachineLearningServices/workspaces/experiments/delete
Get a run and related data and metadata, get a list of all values for the specified metric for a given run, list artifacts for a run Microsoft.MachineLearningServices/workspaces/experiments/runs/read
Create a new run within an experiment, delete runs, restore deleted runs, log metrics under the current run, set tags on a run, delete tags on a run, log params (key-value pair) used for a run, log a batch of metrics, params, and tags for a run, update run status Microsoft.MachineLearningServices/workspaces/experiments/runs/write
Get registered model by name, fetch a list of all registered models in the registry, search for registered models, latest version models for each requests stage, get a registered model's version, search model versions, get URI where a model version's artifacts are stored, search for runs by experiment ids Microsoft.MachineLearningServices/workspaces/models/read
Create a new registered model, update a registered model's name/description, rename existing registered model, create new version of the model, update a model version's description, transition a registered model to one of the stages Microsoft.MachineLearningServices/workspaces/models/write
Delete a registered model along with all its version, delete specific versions of a registered model Microsoft.MachineLearningServices/workspaces/models/delete

Example custom roles

Data scientist

Allows a data scientist to perform all operations inside a workspace except:

  • Creation of compute
  • Deploying models to a production AKS cluster
  • Deploying a pipeline endpoint in production

data_scientist_custom_role.json :

{
    "Name": "Data Scientist Custom",
    "IsCustom": true,
    "Description": "Can run experiment but can't create or delete compute or deploy production endpoints.",
    "Actions": [
        "Microsoft.MachineLearningServices/workspaces/*/read",
        "Microsoft.MachineLearningServices/workspaces/*/action",
        "Microsoft.MachineLearningServices/workspaces/*/delete",
        "Microsoft.MachineLearningServices/workspaces/*/write"
    ],
    "NotActions": [
        "Microsoft.MachineLearningServices/workspaces/delete",
        "Microsoft.MachineLearningServices/workspaces/write",
        "Microsoft.MachineLearningServices/workspaces/computes/*/write",
        "Microsoft.MachineLearningServices/workspaces/computes/*/delete", 
        "Microsoft.Authorization/*",
        "Microsoft.MachineLearningServices/workspaces/computes/listKeys/action",
        "Microsoft.MachineLearningServices/workspaces/listKeys/action",
        "Microsoft.MachineLearningServices/workspaces/services/aks/write",
        "Microsoft.MachineLearningServices/workspaces/services/aks/delete",
        "Microsoft.MachineLearningServices/workspaces/endpoints/pipelines/write"
    ],
    "AssignableScopes": [
        "/subscriptions/<subscription_id>"
    ]
}

Data scientist restricted

A more restricted role definition without wildcards in the allowed actions. It can perform all operations inside a workspace except:

  • Creation of compute
  • Deploying models to a production AKS cluster
  • Deploying a pipeline endpoint in production

data_scientist_restricted_custom_role.json :

{
    "Name": "Data Scientist Restricted Custom",
    "IsCustom": true,
    "Description": "Can run experiment but can't create or delete compute or deploy production endpoints",
    "Actions": [
        "Microsoft.MachineLearningServices/workspaces/*/read",
        "Microsoft.MachineLearningServices/workspaces/computes/start/action",
        "Microsoft.MachineLearningServices/workspaces/computes/stop/action",
        "Microsoft.MachineLearningServices/workspaces/computes/restart/action",
        "Microsoft.MachineLearningServices/workspaces/computes/applicationaccess/action",
        "Microsoft.MachineLearningServices/workspaces/notebooks/storage/read",
        "Microsoft.MachineLearningServices/workspaces/notebooks/storage/write",
        "Microsoft.MachineLearningServices/workspaces/notebooks/storage/delete",
        "Microsoft.MachineLearningServices/workspaces/notebooks/samples/read",
        "Microsoft.MachineLearningServices/workspaces/experiments/runs/write",
        "Microsoft.MachineLearningServices/workspaces/experiments/write",
        "Microsoft.MachineLearningServices/workspaces/experiments/runs/submit/action",
        "Microsoft.MachineLearningServices/workspaces/pipelinedrafts/write",
        "Microsoft.MachineLearningServices/workspaces/metadata/snapshots/write",
        "Microsoft.MachineLearningServices/workspaces/metadata/artifacts/write",
        "Microsoft.MachineLearningServices/workspaces/environments/write",
        "Microsoft.MachineLearningServices/workspaces/models/write",
        "Microsoft.MachineLearningServices/workspaces/modules/write",
        "Microsoft.MachineLearningServices/workspaces/datasets/registered/write", 
        "Microsoft.MachineLearningServices/workspaces/datasets/registered/delete",
        "Microsoft.MachineLearningServices/workspaces/datasets/unregistered/write",
        "Microsoft.MachineLearningServices/workspaces/datasets/unregistered/delete",
        "Microsoft.MachineLearningServices/workspaces/computes/listNodes/action",
        "Microsoft.MachineLearningServices/workspaces/environments/build/action"
    ],
    "NotActions": [
        "Microsoft.MachineLearningServices/workspaces/computes/write",
        "Microsoft.MachineLearningServices/workspaces/write",
        "Microsoft.MachineLearningServices/workspaces/computes/delete",
        "Microsoft.MachineLearningServices/workspaces/delete",
        "Microsoft.MachineLearningServices/workspaces/computes/listKeys/action",
        "Microsoft.MachineLearningServices/workspaces/listKeys/action",
        "Microsoft.Authorization/*",
        "Microsoft.MachineLearningServices/workspaces/datasets/registered/profile/read",
        "Microsoft.MachineLearningServices/workspaces/datasets/registered/preview/read",
        "Microsoft.MachineLearningServices/workspaces/datasets/unregistered/profile/read",
        "Microsoft.MachineLearningServices/workspaces/datasets/unregistered/preview/read",
        "Microsoft.MachineLearningServices/workspaces/datasets/registered/schema/read",    
        "Microsoft.MachineLearningServices/workspaces/datasets/unregistered/schema/read",
        "Microsoft.MachineLearningServices/workspaces/datastores/write",
        "Microsoft.MachineLearningServices/workspaces/datastores/delete"
    ],
    "AssignableScopes": [
        "/subscriptions/<subscription_id>"
    ]
}

MLflow data scientist

Allows a data scientist to perform all MLflow AzureML supported operations except:

  • Creation of compute
  • Deploying models to a production AKS cluster
  • Deploying a pipeline endpoint in production

mlflow_data_scientist_custom_role.json :

{
    "Name": "MLFlow Data Scientist Custom",
    "IsCustom": true,
    "Description": "Can perform azureml mlflow integrated functionalities that includes mlflow tracking, projects, model registry",
    "Actions": [
        "Microsoft.MachineLearningServices/workspaces/experiments/read",
        "Microsoft.MachineLearningServices/workspaces/experiments/write",
        "Microsoft.MachineLearningServices/workspaces/experiments/delete",
        "Microsoft.MachineLearningServices/workspaces/experiments/runs/read",
        "Microsoft.MachineLearningServices/workspaces/experiments/runs/write",
        "Microsoft.MachineLearningServices/workspaces/models/read",
        "Microsoft.MachineLearningServices/workspaces/models/write",
        "Microsoft.MachineLearningServices/workspaces/models/delete"
    ],
    "NotActions": [
        "Microsoft.MachineLearningServices/workspaces/delete",
        "Microsoft.MachineLearningServices/workspaces/write",
        "Microsoft.MachineLearningServices/workspaces/computes/*/write",
        "Microsoft.MachineLearningServices/workspaces/computes/*/delete", 
        "Microsoft.Authorization/*",
        "Microsoft.MachineLearningServices/workspaces/computes/listKeys/action",
        "Microsoft.MachineLearningServices/workspaces/listKeys/action",
        "Microsoft.MachineLearningServices/workspaces/services/aks/write",
        "Microsoft.MachineLearningServices/workspaces/services/aks/delete",
        "Microsoft.MachineLearningServices/workspaces/endpoints/pipelines/write"
    ],
    "AssignableScopes": [
        "/subscriptions/<subscription_id>"
    ]
}

MLOps

Allows you to assign a role to a service principal and use that to automate your MLOps pipelines. For example, to submit runs against an already published pipeline:

mlops_custom_role.json :

{
    "Name": "MLOps Custom",
    "IsCustom": true,
    "Description": "Can run pipelines against a published pipeline endpoint",
    "Actions": [
        "Microsoft.MachineLearningServices/workspaces/read",
        "Microsoft.MachineLearningServices/workspaces/endpoints/pipelines/read",
        "Microsoft.MachineLearningServices/workspaces/metadata/artifacts/read",
        "Microsoft.MachineLearningServices/workspaces/metadata/snapshots/read",
        "Microsoft.MachineLearningServices/workspaces/environments/read",    
        "Microsoft.MachineLearningServices/workspaces/metadata/secrets/read",
        "Microsoft.MachineLearningServices/workspaces/modules/read",
        "Microsoft.MachineLearningServices/workspaces/experiments/runs/read",
        "Microsoft.MachineLearningServices/workspaces/datasets/registered/read",
        "Microsoft.MachineLearningServices/workspaces/datastores/read",
        "Microsoft.MachineLearningServices/workspaces/environments/write",
        "Microsoft.MachineLearningServices/workspaces/experiments/runs/write",
        "Microsoft.MachineLearningServices/workspaces/metadata/artifacts/write",
        "Microsoft.MachineLearningServices/workspaces/metadata/snapshots/write",
        "Microsoft.MachineLearningServices/workspaces/environments/build/action",
        "Microsoft.MachineLearningServices/workspaces/experiments/runs/submit/action"
    ],
    "NotActions": [
        "Microsoft.MachineLearningServices/workspaces/computes/write",
        "Microsoft.MachineLearningServices/workspaces/write",
        "Microsoft.MachineLearningServices/workspaces/computes/delete",
        "Microsoft.MachineLearningServices/workspaces/delete",
        "Microsoft.MachineLearningServices/workspaces/computes/listKeys/action",
        "Microsoft.MachineLearningServices/workspaces/listKeys/action",
        "Microsoft.Authorization/*"
    ],
    "AssignableScopes": [
        "/subscriptions/<subscription_id>"
    ]
}

Workspace Admin

Allows you to perform all operations within the scope of a workspace, except:

  • Creating a new workspace
  • Assigning subscription or workspace level quotas

The workspace admin also cannot create a new role. It can only assign existing built-in or custom roles within the scope of their workspace:

workspace_admin_custom_role.json :

{
    "Name": "Workspace Admin Custom",
    "IsCustom": true,
    "Description": "Can perform all operations except quota management and upgrades",
    "Actions": [
        "Microsoft.MachineLearningServices/workspaces/*/read",
        "Microsoft.MachineLearningServices/workspaces/*/action",
        "Microsoft.MachineLearningServices/workspaces/*/write",
        "Microsoft.MachineLearningServices/workspaces/*/delete",
        "Microsoft.Authorization/roleAssignments/*"
    ],
    "NotActions": [
        "Microsoft.MachineLearningServices/workspaces/write"
    ],
    "AssignableScopes": [
        "/subscriptions/<subscription_id>"
    ]
}

Data labeler

Allows you to define a role scoped only to labeling data:

labeler_custom_role.json :

{
    "Name": "Labeler Custom",
    "IsCustom": true,
    "Description": "Can label data for Labeling",
    "Actions": [
        "Microsoft.MachineLearningServices/workspaces/read",
        "Microsoft.MachineLearningServices/workspaces/labeling/projects/read",
        "Microsoft.MachineLearningServices/workspaces/labeling/labels/write"
    ],
    "NotActions": [
        "Microsoft.MachineLearningServices/workspaces/labeling/projects/summary/read"
    ],
    "AssignableScopes": [
        "/subscriptions/<subscription_id>"
    ]
}

Labeling Team Lead

Allows you to review and reject the labeled dataset and view labeling insights. In addition to it, this role also allows you to perform the role of a labeler.

labeling_team_lead_custom_role.json :

{
    "properties": {
        "roleName": "Labeling Team Lead",
        "description": "Team lead for Labeling Projects",
        "assignableScopes": [
            "/subscriptions/<subscription_id>"
        ],
        "permissions": [
            {
                "actions": [
                    "Microsoft.MachineLearningServices/workspaces/read",
                    "Microsoft.MachineLearningServices/workspaces/labeling/labels/read",
                    "Microsoft.MachineLearningServices/workspaces/labeling/labels/write",
                    "Microsoft.MachineLearningServices/workspaces/labeling/labels/reject/action",
                    "Microsoft.MachineLearningServices/workspaces/labeling/projects/read",
                    "Microsoft.MachineLearningServices/workspaces/labeling/projects/summary/read"
                ],
                "notActions": [
                    "Microsoft.MachineLearningServices/workspaces/labeling/projects/write",
                    "Microsoft.MachineLearningServices/workspaces/labeling/projects/delete",
                    "Microsoft.MachineLearningServices/workspaces/labeling/export/action"
                ],
                "dataActions": [],
                "notDataActions": []
            }
        ]
    }
}

Troubleshooting

Here are a few things to be aware of while you use Azure role-based access control (Azure RBAC):

  • When you create a resource in Azure, such as a workspace, you are not directly the owner of the resource. Your role is inherited from the highest scope role that you are authorized against in that subscription. As an example if you are a Network Administrator, and have the permissions to create a Machine Learning workspace, you would be assigned the Network Administrator role against that workspace, and not the Owner role.

  • To perform quota operations in a workspace, you need subscription level permissions. This means setting either subscription level quota or workspace level quota for your managed compute resources can only happen if you have write permissions at the subscription scope.

  • When there are two role assignments to the same Azure Active Directory user with conflicting sections of Actions/NotActions, your operations listed in NotActions from one role might not take effect if they are also listed as Actions in another role. To learn more about how Azure parses role assignments, read How Azure RBAC determines if a user has access to a resource

  • To deploy your compute resources inside a VNet, you need to explicitly have permissions for the following actions:

    • Microsoft.Network/virtualNetworks/*/read on the VNet resources.
    • Microsoft.Network/virtualNetworks/subnets/join/action on the subnet resource.

    For more information on Azure RBAC with networking, see the Networking built-in roles.

  • It can sometimes take up to 1 hour for your new role assignments to take effect over cached permissions across the stack.

  • Conditional access is currently not supported with Azure Machine Learning.

Next steps