Skip to main content
Version: 1.42

Configure Azure Machine Learning

With the Azure Machine Learning integration, you can manage, monitor, and explain your Azure Machine Learning deployments in Deeploy.

Configure Azure Machine Learning​

The Azure Machine Learning integration is controlled on team level. Admins can set up the integration on the Integrations page, which is part of the Admin panel. Click Configure on the Azure Machine Learning card and Add credentials to set up the integration.

Pre-requisites​

An Azure Machine Learning workspace is required to be able to create deployments. Furthermore an Azure AD app is required which allows Deeploy to connect to your Azure Machine Learning workspace.

Making the integration​

The following information is needed to set-up the Azure Machine learning integration:

  • Azure tenant ID, Azure subscription ID and Azure resource group name You can find these in the portal.
  • Azure client ID and Azure client secret Deeploy needs Azure AD app credentials to access the necessary resources on your Azure account. To get your Azure credentials, create an Azure client secret in your Azure AD app. Your app needs a role with the following minimal permissions:
"permissions": [
    {
        "actions": [
            "Microsoft.MachineLearningServices/workspaces/environments/*",
            "Microsoft.MachineLearningServices/workspaces/onlineEndpoints/*",
            "Microsoft.MachineLearningServices/workspaces/models/*/read",
            "Microsoft.MachineLearningServices/workspaces/read",
            "Microsoft.MachineLearningServices/workspaces/metadata/*"
        ],
        "notActions": [],
        "dataActions": [],
        "notDataActions": []
    }
]
  • Workspace permissions Define which Workspaces should have access to your Azure Machine Learning credentials. You can assign your Azure Machine Learning credentials to multiple Workspaces, but each Workspace can only have one Azure Machine Learning credentials assigned to it.

Update Azure Machine Learning credentials​

To update your Azure Machine Learning credentials, click on the Actions button in your Azure Machine Learning credentials table, then click Update. Updating your credentials always requires you to fill in your client secret.

Changing Workspace permissions​

To change the Workspaces to which the Azure Machine Learning credentials are assigned to, click on the Actions button in your Azure Machine Learning credentials table, then click Manage permissions. Now select or deselect the Workspaces you want to change, and click Save.

Delete Azure Machine Learning credentials​

To delete your Azure Machine Learning credentials, click on the Actions button in your Azure Machine Learning credentials table, then click Delete. Click Delete in the dialog to confirm the deletion of your credentials.

Get started with Azure Machine Learning for Deployments​

You can get started with Azure Machine Learning Deployments by either making Azure Machine Learning your default deployment service, or creating single Azure Machine Learning Deployments. Both options are detailed in this section.

A guide to creating Deployments with Azure Machine Learning as a deployment service is available in Creating Azure Machine Learning Deployments.

Azure Machine Learning as your default deployment service​

The default deployment service is controlled on a Workspace level. Workspace admins can change the default deployment service to Azure Machine Learning in the Workspace settings. All Deployments use the default deployment service, unless specified otherwise when creating a Deployment.

Create a single Azure Machine Learning Deployment​

It's possible to create a Deployment using Azure Machine Learning, when the default deployment service is set to KServe or SageMaker (and vice versa). To change the deployment service for a single Deployment, untoggle the Use default deployment service settings toggle in the Deployment step when creating your Deployment, and choose Azure Machine Learning as your deployment service.