Update how-to-assign-roles.md

Author: rastala

articles/machine-learning/how-to-assign-roles.md

@@ -40,6 +40,12 @@ Azure Machine Learning workspaces have a five built-in roles that are available
 | **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](how-to-manage-registries.md) have a **AzureML Registry User** role that can be assigned to a registy resource to grant data scientists user-level prermissions. For administrator-level permissions to create or delete registries, use **Contributor** or **Owner** role.
+
+| Role | Access level |
+| --- | --- |
+| **AzureML Registry User** | Can get registries, and read, write and delete assets within them. Cannot create new registy 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 **Azure ML Compute Operator** roles to permit the user to perform experiments while creating computes in a self-service manner.
 
 > [!IMPORTANT]