Use GitHub Actions with Azure Machine Learning

APPLIES TO: Azure CLI ml extension v2 (current) Python SDK azure-ai-ml v2 (current)

Get started with GitHub Actions to train a model on Azure Machine Learning.

This article will teach you how to create a GitHub Actions workflow that builds and deploys a machine learning model to Azure Machine Learning. You'll train a scikit-learn linear regression model on the NYC Taxi dataset.

GitHub Actions uses a workflow YAML (.yml) file in the /.github/workflows/ path in your repository. This definition contains the various steps and parameters that make up the workflow.


Before following the steps in this article, make sure you have the following prerequisites:

  • An Azure Machine Learning workspace. If you don't have one, use the steps in the Quickstart: Create workspace resources article to create one.

  • To install the Python SDK v2, use the following command:

    pip install azure-ai-ml azure-identity

    To update an existing installation of the SDK to the latest version, use the following command:

    pip install --upgrade azure-ai-ml azure-identity

    For more information, see Install the Python SDK v2 for Azure Machine Learning.

  • A GitHub account. If you don't have one, sign up for free.

Step 1: Get the code

Fork the following repo at GitHub:

Clone your forked repo locally.

git clone

Step 2: Authenticate with Azure

You'll need to first define how to authenticate with Azure. You can use a service principal or OpenID Connect.

Generate deployment credentials

Create a service principal with the az ad sp create-for-rbac command in the Azure CLI. Run this command with Azure Cloud Shell in the Azure portal or by selecting the Try it button.

az ad sp create-for-rbac --name "myML" --role contributor \
                            --scopes /subscriptions/<subscription-id>/resourceGroups/<group-name> \

The parameter --json-auth is available in Azure CLI versions >= 2.51.0. Versions prior to this use --sdk-auth with a deprecation warning.

In the example above, replace the placeholders with your subscription ID, resource group name, and app name. The output is a JSON object with the role assignment credentials that provide access to your App Service app similar to below. Copy this JSON object for later.

    "clientId": "<GUID>",
    "clientSecret": "<GUID>",
    "subscriptionId": "<GUID>",
    "tenantId": "<GUID>",

Create secrets

  1. In GitHub, go to your repository.

  2. Go to Settings in the navigation menu.

  3. Select Security > Secrets and variables > Actions.

    Screenshot of adding a secret

  4. Select New repository secret.

  5. Paste the entire JSON output from the Azure CLI command into the secret's value field. Give the secret the name AZURE_CREDENTIALS.

  6. Select Add secret.

Step 3: Update to connect to your Azure Machine Learning workspace

You'll need to update the CLI setup file variables to match your workspace.

  1. In your forked repository, go to azureml-examples/cli/.

  2. Edit and update these variables in the file.

    Variable Description
    GROUP Name of resource group
    LOCATION Location of your workspace (example: eastus2)
    WORKSPACE Name of Azure Machine Learning workspace

Step 4: Update pipeline.yml with your compute cluster name

You'll use a pipeline.yml file to deploy your Azure Machine Learning pipeline. This is a machine learning pipeline and not a DevOps pipeline. You only need to make this update if you're using a name other than cpu-cluster for your computer cluster name.

  1. In your forked repository, go to azureml-examples/cli/jobs/pipelines/nyc-taxi/pipeline.yml.
  2. Each time you see compute: azureml:cpu-cluster, update the value of cpu-cluster with your compute cluster name. For example, if your cluster is named my-cluster, your new value would be azureml:my-cluster. There are five updates.

Step 5: Run your GitHub Actions workflow

Your workflow authenticates with Azure, sets up the Azure Machine Learning CLI, and uses the CLI to train a model in Azure Machine Learning.

Your workflow file is made up of a trigger section and jobs:

  • A trigger starts the workflow in the on section. The workflow runs by default on a cron schedule and when a pull request is made from matching branches and paths. Learn more about events that trigger workflows.
  • In the jobs section of the workflow, you checkout code and log into Azure with your service principal secret.
  • The jobs section also includes a setup action that installs and sets up the Machine Learning CLI (v2). Once the CLI is installed, the run job action runs your Azure Machine Learning pipeline.yml file to train a model with NYC taxi data.

Enable your workflow

  1. In your forked repository, open .github/workflows/cli-jobs-pipelines-nyc-taxi-pipeline.yml and verify that your workflow looks like this.

    name: cli-jobs-pipelines-nyc-taxi-pipeline
        - cron: "0 0/4 * * *"
          - main
          - sdk-preview
          - cli/jobs/pipelines/nyc-taxi/**
          - .github/workflows/cli-jobs-pipelines-nyc-taxi-pipeline.yml
          - cli/
          - cli/
        runs-on: ubuntu-latest
        - name: check out repo
          uses: actions/checkout@v2
        - name: azure login
          uses: azure/login@v1
            creds: ${{secrets.AZURE_CREDENTIALS}}
        - name: setup
          run: bash
          working-directory: cli
          continue-on-error: true
        - name: run job
          run: bash -x ../../../ pipeline.yml
          working-directory: cli/jobs/pipelines/nyc-taxi
  2. Select View runs.

  3. Enable workflows by selecting I understand my workflows, go ahead and enable them.

  4. Select the cli-jobs-pipelines-nyc-taxi-pipeline workflow and choose to Enable workflow. Screenshot of enable GitHub Actions workflow.

  5. Select Run workflow and choose the option to Run workflow now. Screenshot of run GitHub Actions workflow.

Step 6: Verify your workflow run

  1. Open your completed workflow run and verify that the build job ran successfully. You'll see a green checkmark next to the job.

  2. Open Azure Machine Learning studio and navigate to the nyc-taxi-pipeline-example. Verify that each part of your job (prep, transform, train, predict, score) completed and that you see a green checkmark.

    Screenshot of successful Machine Learning Studio run.

Clean up resources

When your resource group and repository are no longer needed, clean up the resources you deployed by deleting the resource group and your GitHub repository.

Next steps