|**Azure Machine Learning permissions** | 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 default roles. You can add users to the workspace and assign them to one of these built-in roles. For more information, see [Azure Machine Learning default roles](../machine-learning/how-to-assign-roles.md) and [Azure built-in roles](../role-based-access-control/built-in-roles.md). <br><br> **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](../role-based-access-control/overview.md). <br><br>If you're an owner of an Azure ML workspace, you can add and remove roles for the workspace and assign roles to users. For more information, see:<br> - [Azure portal](../role-based-access-control/role-assignments-portal.yml)<br> - [PowerShell](../role-based-access-control/role-assignments-powershell.md)<br> - [Azure CLI](../role-based-access-control/role-assignments-cli.md)<br> - [REST API](../role-based-access-control/role-assignments-rest.md)<br> - [Azure Resource Manager templates](../role-based-access-control/role-assignments-template.md)<br> - [Azure Machine Learning CLI ](../machine-learning/how-to-assign-roles.md#manage-workspace-access)<br><br>If the built-in roles are insufficient, you can also 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. For more information, see [Create custom role](../machine-learning/how-to-assign-roles.md#create-custom-role). |
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