Manage access to Azure Machine Learning workspaces

Article
12/20/2024

This article explains how to manage access (authorization) to Azure Machine Learning workspaces. You can use Azure role-based access control (Azure RBAC) to manage access to Azure resources, giving users the ability to create new resources or use existing ones. Users in your Microsoft Entra ID 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 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 might be useful with Azure Machine Learning, see the following links:

Warning

Applying some roles might limit UI functionality in Azure Machine Learning studio for other users. For example, if a user's role doesn't have the ability to create a compute instance, the option to create a compute instance isn't 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 built-in roles that are available by default. When adding users to a workspace, they can be assigned one of the following roles.

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.
AzureML Compute Operator	Can create, manage, delete, and access compute resources within a workspace.
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.

In addition, Azure Machine Learning registries have an AzureML Registry User role that can be assigned to a registry resource to grant user-level permissions to data scientists. For administrator-level permissions to create or delete registries, use the Contributor or Owner role.

Role	Access level
AzureML Registry User	Can get registries, and read, write, and delete assets within them. Can't create new registry resources or delete them.

You can combine the roles to grant different levels of access. For example, you can grant a workspace user both AzureML Data Scientist and AzureML Compute Operator roles to permit the user to perform experiments while creating computes in a self-service manner.

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.

Azure AI Administrator role

Prior to 11/19/2024, the system-assigned managed identity created for the workspace was automatically assigned the Contributor role for the resource group that contains the workspace. Workspaces created after this date have the system-assigned managed identity assigned to the Azure AI Administrator role. This role is more narrowly scoped to the minimum permissions needed for the managed identity to perform its tasks.

The Azure AI Administrator role is currently in public preview.

Important

This feature is currently in public preview. This preview version is provided without a service-level agreement, and we don't recommend it for production workloads. Certain features might not be supported or might have constrained capabilities.

For more information, see Supplemental Terms of Use for Azure Previews.

The Azure AI Administrator role has the following permissions:

{
    "permissions": [
        {
            "actions": [
                "Microsoft.Authorization/*/read",
                "Microsoft.CognitiveServices/*",
                "Microsoft.ContainerRegistry/registries/*",
                "Microsoft.DocumentDb/databaseAccounts/*",
                "Microsoft.Features/features/read",
                "Microsoft.Features/providers/features/read",
                "Microsoft.Features/providers/features/register/action",
                "Microsoft.Insights/alertRules/*",
                "Microsoft.Insights/components/*",
                "Microsoft.Insights/diagnosticSettings/*",
                "Microsoft.Insights/generateLiveToken/read",
                "Microsoft.Insights/logDefinitions/read",
                "Microsoft.Insights/metricAlerts/*",
                "Microsoft.Insights/metricdefinitions/read",
                "Microsoft.Insights/metrics/read",
                "Microsoft.Insights/scheduledqueryrules/*",
                "Microsoft.Insights/topology/read",
                "Microsoft.Insights/transactions/read",
                "Microsoft.Insights/webtests/*",
                "Microsoft.KeyVault/*",
                "Microsoft.MachineLearningServices/workspaces/*",
                "Microsoft.Network/virtualNetworks/subnets/joinViaServiceEndpoint/action",
                "Microsoft.ResourceHealth/availabilityStatuses/read",
                "Microsoft.Resources/deployments/*",
                "Microsoft.Resources/deployments/operations/read",
                "Microsoft.Resources/subscriptions/operationresults/read",
                "Microsoft.Resources/subscriptions/read",
                "Microsoft.Resources/subscriptions/resourcegroups/deployments/*",
                "Microsoft.Resources/subscriptions/resourceGroups/read",
                "Microsoft.Resources/subscriptions/resourceGroups/write",
                "Microsoft.Storage/storageAccounts/*",
                "Microsoft.Support/*",
                "Microsoft.Search/searchServices/write",
                "Microsoft.Search/searchServices/read",
                "Microsoft.Search/searchServices/delete",
                "Microsoft.Search/searchServices/indexes/*",
                "Microsoft.DataFactory/factories/*"
            ],
            "notActions": [],
            "dataActions": [],
            "notDataActions": []
        }
    ]
}

Convert an existing system-managed identity to the Azure AI Administrator role

Tip

We recommend that you convert workspaces created before 11/19/2024 to use the Azure AI Administrator role. The Azure AI Administrator role is more narrowly scoped than the previously used Contributor role and follows the principal of least privilege.

You can convert workspaces created before 11/19/2024 by using one of the following methods:

Azure REST API: Use a PATCH request to the Azure REST API for the workspace. The body of the request should set {"properties":{"allowRoleAssignmeentOnRG":true}}. The following example shows a PATCH request using curl. Replace <your-subscription>, <resource-group-name>, <workspace-name>, and <YOUR-ACCESS-TOKEN> with the values for your scenario. For more information on using REST APIs, visit the Azure REST API documentation.
```
curl -X PATCH https://management.chinacloudapi.cn/subscriptions/<your-subscription>/resourcegroups/<resource-group-name>/providers/Microsoft.MachineLearningServices/workspaces/<workspace-name>?api-version=2024-04-01-preview -H "Authorization:Bearer <YOUR-ACCESS-TOKEN>"
```
Azure CLI: Use the az ml workspace update command with the --allow-roleassignment-on-rg true parameter. The following example updates a workspace named myworkspace. This command requires the Azure Machine Learning CLI extension version 2.27.0 or later.
```
az ml workspace update --name myworkspace --allow-roleassignment-on-rg true
```
Azure Python SDK: Set the allow_roleassignment_on_rg property of the Workspace object to True and then perform an update operation. The following example updates a workspace named myworkspace. This operation requires the Azure Machine Learning SDK version 1.17.0 or later.
```
ws = ml_client.workspaces.get(name="myworkspace")
ws.allow_roleassignment_on_rg = True
ws = ml_client.workspaces.begin_update(workspace=ws).result()
```

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:

For example, use the Azure CLI to assign Contributor role to joe@contoso.com for resource group this-rg with the following command:

az role assignment create --role "Contributor" --assignee "joe@contoso.com" --resource-group this-rg

Use Microsoft Entra security groups to manage workspace access

You can use Microsoft Entra security groups to manage access to workspaces. This approach has following benefits:

Team or project leaders can manage user access to workspace as security group owners, without needing Owner role on the workspace resource directly.
You can organize, manage and revoke users' permissions on workspace and other resources as a group, without having to manage permissions on user-by-user basis.

To use Microsoft Entra security groups:

Create a security group.
Add a group owner. This user has permissions to add or remove group members. The group owner isn't required to be group member, or have direct RBAC role on the workspace.
Assign the group an RBAC role on the workspace, such as AzureML Data Scientist, Reader, or Contributor.
Add group members. The members gain access to the workspace.

Create custom role

If the built-in roles are insufficient, you can create custom roles. Custom roles might possess 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 Data Scientist Custom role 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/<subscriptionId>/resourceGroups/<resourceGroupName>/providers/Microsoft.MachineLearningServices/workspaces/<workspaceName>"
    ]
}

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

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

az role definition create --role-definition data_scientist_custom_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 <subscriptionId> --custom-role-only true

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

az role definition list -n <roleName> --subscription <subscriptionId>

Update a custom role

In the Azure CLI, run the following command:

az role definition update --role-definition update_def.json --subscription <subscriptionId>

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'll need to recreate complex role assignments, an Azure Resource Manager template can be a significant 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 also work automatically. For certain activities, the permissions differ between V1 and V2 APIs.

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 ₁	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 (V1)	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`
Submitting any type of run (V2)	Not required	Not required	Owner, contributor, or custom role allowing: `/workspaces//read`, `/workspaces/environments/write`, `/workspaces/jobs/`, `/workspaces/metadata/artifacts/write`, `/workspaces/metadata/codes/*/write`, `/workspaces/environments/build/action`, `/workspaces/environments/readSecrets/action`
Publishing pipelines and endpoints (V1)	Not required	Not required	Owner, contributor, or custom role allowing: `/workspaces/endpoints/pipelines/`, `/workspaces/pipelinedrafts/`, `/workspaces/modules/*`
Publishing pipelines and endpoints (V2)	Not required	Not required	Owner, contributor, or custom role allowing: `/workspaces/endpoints/pipelines/`, `/workspaces/pipelinedrafts/`, `/workspaces/components/*`
Attach an AKS resource ₂	Not required	Owner or contributor on the resource group that contains AKS
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 don't use Microsoft Entra auth) OR `/workspaces/read` (when you use 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`, `/workspaces/listNotebookAccessToken/read`
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`
Create/manage online endpoints and deployments	Not required	To deploy on studio, `Microsoft.Resources/deployments/write`	Owner, contributor, or custom role allowing `Microsoft.MachineLearningServices/workspaces/onlineEndpoints/*`.
Retrieve authentication credentials for online endpoints	Not required	Not required	Owner, contributor, or custom role allowing `Microsoft.MachineLearningServices/workspaces/onlineEndpoints/token/action` and `Microsoft.MachineLearningServices/workspaces/onlineEndpoints/listkeys/action`

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.
When attaching an AKS cluster, you also need to have the Azure Kubernetes Service Cluster Admin Role on the cluster.

Deploy into a virtual network or subnet

To deploy resources into a virtual network or subnet, your user account must have permissions to the following actions in Azure role-based access control (Azure RBAC):
- "Microsoft.Network/*/read" on the virtual network resource. This permission isn't needed for Azure Resource Manager (ARM) template deployments.
- "Microsoft.Network/virtualNetworks/join/action" on the virtual network resource.
- "Microsoft.Network/virtualNetworks/subnets/join/action" on the subnet resource.
For more information on Azure RBAC with networking, see the Networking built-in roles

Differences between actions for V1 and V2 APIs

There are certain differences between actions for V1 APIs and V2 APIs.

Asset	Action path for V1 API	Action path for V2 API
Dataset	Microsoft.MachineLearningServices/workspaces/datasets	Microsoft.MachineLearningServices/workspaces/datasets/versions
Experiment runs and jobs	Microsoft.MachineLearningServices/workspaces/experiments	Microsoft.MachineLearningServices/workspaces/jobs
Models	Microsoft.MachineLearningServices/workspaces/models	Microsoft.MachineLearningServices/workspaces/models/versions
Snapshots and code	Microsoft.MachineLearningServices/workspaces/snapshots	Microsoft.MachineLearningServices/workspaces/codes/versions
Modules and components	Microsoft.MachineLearningServices/workspaces/modules	Microsoft.MachineLearningServices/workspaces/components

You can make custom roles compatible with both V1 and V2 APIs by including both actions, or using wildcards that include both actions, for example Microsoft.MachineLearningServices/workspaces/datasets/*/read.

Create a workspace using a customer-managed key

When using a customer-managed key (CMK), an Azure Key Vault is used to store the key. The user or service principal used to create the workspace must have owner or contributor access to the key vault.

If your workspace is configured with a user-assigned managed identity, the identity must be granted the following roles. These roles allow the managed identity to create the Azure Storage, Azure Cosmos DB, and Azure Search resources used when using a customer-managed key:

Microsoft.Storage/storageAccounts/write
Microsoft.Search/searchServices/write
Microsoft.DocumentDB/databaseAccounts/write

Within the key vault, the user or service principal must have create, get, delete, and purge access to the key through a key vault access policy. For more information, see Azure Key Vault security.

User-assigned managed identity with Azure Machine Learning 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
(V1) List, read, create, update or delete experiments	`Microsoft.MachineLearningServices/workspaces/experiments/*`
(V2) List, read, create, update or delete jobs	`Microsoft.MachineLearningServices/workspaces/jobs/*`
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/<subscriptionId>"
    ]
}

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/write",
        "Microsoft.MachineLearningServices/workspaces/notebooks/storage/delete",
        "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/components/*/write",
        "Microsoft.MachineLearningServices/workspaces/datasets/*/write", 
        "Microsoft.MachineLearningServices/workspaces/datasets/*/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/connections/listsecrets/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/<subscriptionId>"
    ]
}

MLflow data scientist

Allows a data scientist to perform all MLflow Azure Machine Learning 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/*",
        "Microsoft.MachineLearningServices/workspaces/jobs/*",
        "Microsoft.MachineLearningServices/workspaces/models/*"
    ],
    "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/<subscriptionId>"
    ]
}

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/components/read",       
        "Microsoft.MachineLearningServices/workspaces/datasets/*/read",
        "Microsoft.MachineLearningServices/workspaces/datastores/read",
        "Microsoft.MachineLearningServices/workspaces/environments/write",
        "Microsoft.MachineLearningServices/workspaces/experiments/runs/read",       
        "Microsoft.MachineLearningServices/workspaces/experiments/runs/write",
        "Microsoft.MachineLearningServices/workspaces/experiments/runs/submit/action",
        "Microsoft.MachineLearningServices/workspaces/experiments/jobs/read",       
        "Microsoft.MachineLearningServices/workspaces/experiments/jobs/write",
        "Microsoft.MachineLearningServices/workspaces/metadata/artifacts/write",
        "Microsoft.MachineLearningServices/workspaces/metadata/snapshots/write",
        "Microsoft.MachineLearningServices/workspaces/metadata/codes/*/write",       
        "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/*"
    ],
    "AssignableScopes": [
        "/subscriptions/<subscriptionId>"
    ]
}

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/<subscriptionId>"
    ]
}

Data labeling

There is a built-in role for data labeling, scoped only to labeling data. The following custom roles give other levels of access for a data labeling project.

The 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 :

{
    "Name": "Labeling Team Lead",
    "IsCustom": true,
    "Description": "Team lead for Labeling Projects",
    "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/labels/update/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"
    ],
    "AssignableScopes": [
        "/subscriptions/<subscriptionId>"
    ]
}

A vendor account manager can help manage all the vendor roles and perform any labeling action. They can't modify projects or view MLAssist experiments.

vendor_admin_role.json :

{
    "Name": "Vendor account admin",
    "IsCustom": true,
    "Description": "Vendor account admin for Labeling Projects",
    "Actions": [
        "Microsoft.MachineLearningServices/workspaces/read", 
        "Microsoft.MachineLearningServices/workspaces/experiments/runs/read", 
        "Microsoft.MachineLearningServices/workspaces/labeling/labels/read", 
        "Microsoft.MachineLearningServices/workspaces/labeling/labels/write", 
        "Microsoft.MachineLearningServices/workspaces/labeling/labels/reject/action",
        "Microsoft.MachineLearningServices/workspaces/labeling/labels/update/action",
        "Microsoft.MachineLearningServices/workspaces/labeling/labels/approve_unapprove/action",
        "Microsoft.MachineLearningServices/workspaces/labeling/projects/read", 
        "Microsoft.MachineLearningServices/workspaces/labeling/projects/summary/read", 
        "Microsoft.MachineLearningServices/workspaces/labeling/export/action", 
        "Microsoft.MachineLearningServices/workspaces/datasets/registered/read"
    ],
    "AssignableScopes": [
        "/subscriptions/<subscriptionId>"
    ]
}

A customer quality assurance role can view project dashboards, preview datasets, export a labeling project, and review submitted labels. This role can't submit labels.

customer_qa_role.json :

{
    "Name": "Customer QA",
    "IsCustom": true,
    "Description": "Customer QA for Labeling Projects",
    "Actions": [
        "Microsoft.MachineLearningServices/workspaces/read",
        "Microsoft.MachineLearningServices/workspaces/experiments/runs/read",
        "Microsoft.MachineLearningServices/workspaces/labeling/labels/read",
        "Microsoft.MachineLearningServices/workspaces/labeling/labels/reject/action",
        "Microsoft.MachineLearningServices/workspaces/labeling/labels/approve_unapprove/action",
        "Microsoft.MachineLearningServices/workspaces/labeling/projects/read",
        "Microsoft.MachineLearningServices/workspaces/labeling/projects/summary/read",
        "Microsoft.MachineLearningServices/workspaces/labeling/export/action",
        "Microsoft.MachineLearningServices/workspaces/datasets/registered/read"
    ],
    "AssignableScopes": [
        "/subscriptions/<subscriptionId>"
    ]
}

A vendor quality assurance role can perform a customer quality assurance role, but can't preview the dataset.

vendor_qa_role.json:

{
    "Name": "Vendor QA",
    "IsCustom": true,
    "Description": "Vendor QA for Labeling Projects",
    "Actions": [
        "Microsoft.MachineLearningServices/workspaces/read", 
        "Microsoft.MachineLearningServices/workspaces/experiments/runs/read", 
        "Microsoft.MachineLearningServices/workspaces/labeling/labels/read", 
        "Microsoft.MachineLearningServices/workspaces/labeling/labels/reject/action",
        "Microsoft.MachineLearningServices/workspaces/labeling/labels/update/action",
        "Microsoft.MachineLearningServices/workspaces/labeling/labels/approve_unapprove/action",
        "Microsoft.MachineLearningServices/workspaces/labeling/projects/read", 
        "Microsoft.MachineLearningServices/workspaces/labeling/projects/summary/read", 
        "Microsoft.MachineLearningServices/workspaces/labeling/export/action"
    ],
    "AssignableScopes": [
        "/subscriptions/<subscriptionId>"
    ]
}

Troubleshooting

Here are a few things to be aware of while you use Azure RBAC:

When you create a resource in Azure, such as a workspace, you're not directly the owner of the resource. Your role is inherited from the highest scope role that you're authorized against in that subscription. As an example if you're 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.
In order to deploy on studio, you need Microsoft.Resources/deployments/write AND Microsoft.MachineLearningServices/workspaces/onlineEndpoints/deployments/write. For SDK/CLI deployments, you need Microsoft.MachineLearningServices/workspaces/onlineEndpoints/deployments/write. Contact your workspace/resource group owner for the additional permissions.
When there are two role assignments to the same Microsoft Entra user with conflicting sections of Actions/NotActions, your operations listed in NotActions from one role might not take effect if they're 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
It can sometimes take up to one hour for your new role assignments to take effect over cached permissions across the stack.

Manage access to Azure Machine Learning workspaces

Default roles

Azure AI Administrator role

Convert an existing system-managed identity to the Azure AI Administrator role

Manage workspace access

Use Microsoft Entra security groups to manage workspace access

Create custom role

Azure Machine Learning operations

List custom roles

Update a custom role

Use Azure Resource Manager templates for repeatability

Common scenarios

Deploy into a virtual network or subnet

Differences between actions for V1 and V2 APIs

Create a workspace using a customer-managed key

User-assigned managed identity with Azure Machine Learning compute cluster

MLflow operations

Example custom roles

Data scientist

Data scientist restricted

MLflow data scientist

MLOps

Workspace Admin

Data labeling

Troubleshooting

Next steps

Additional resources