Updates for OIDC emphasis

Author: juliakm

articles/machine-learning/how-to-github-actions-machine-learning.md

@@ -8,7 +8,7 @@ ms.subservice: mlops
 author: Blackmist
 ms.author: larryfr
 ms.reviewer: jukullam
-ms.date: 12/06/2023
+ms.date: 02/07/2025
 ms.topic: how-to
 ms.custom: github-actions-azure
 ---
@@ -45,7 +45,8 @@ git clone https://github.com/YOUR-USERNAME/azureml-examples
 
 ## Step 2: Authenticate with Azure
 
-You'll need to first define how to authenticate with Azure. You can use a [service principal](/azure/active-directory/develop/app-objects-and-service-principals#service-principal-object) or [OpenID Connect](https://docs.github.com/en/actions/deployment/security-hardening-your-deployments/about-security-hardening-with-openid-connect). 
+You'll need to first define how to authenticate with Azure. The recommended, more secure option is to [sign in with OpenID Connect using a Microsoft Entra application or a user-assigned managed identity](/azure/developer/github/connect-from-azure-openid-connect). If necessary, you can also use [sign in with a service principal and secret](/azure/developer/github/connect-from-azure-secret). This approach is less secure and not recommended. 
+
 
 ### Generate deployment credentials
 
@@ -79,18 +80,16 @@ You'll use a `pipeline.yml` file to deploy your Azure Machine Learning pipeline.
 
 Your workflow authenticates with Azure, sets up the Azure Machine Learning CLI, and uses the CLI to train a model in Azure Machine Learning. 
 
-# [Service principal](#tab/userlevel)
-
+ # [OpenID Connect](#tab/openid)
 
 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](https://docs.github.com/actions/using-workflows/events-that-trigger-workflows). 
-- In the jobs section of the workflow, you checkout code and log into Azure with your service principal secret.
+- In the jobs section of the workflow, you checkout code and log into Azure with the Azure login action using OpenID Connect.
 - The jobs section also includes a setup action that installs and sets up the [Machine Learning CLI (v2)](how-to-configure-cli.md). 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. 
+1. In your forked repository, open `.github/workflows/cli-jobs-pipelines-nyc-taxi-pipeline.yml` and verify that your workflow looks like this.
 
     ```yaml
     name: cli-jobs-pipelines-nyc-taxi-pipeline
@@ -114,9 +113,11 @@ Your workflow file is made up of a trigger section and jobs:
         - name: check out repo
           uses: actions/checkout@v2
         - name: azure login
-          uses: azure/login@v1
+          uses: azure/login@v2
           with:
-            creds: ${{secrets.AZURE_CREDENTIALS}}
+              client-id: ${{ secrets.AZURE_CLIENT_ID }}
+              tenant-id: ${{ secrets.AZURE_TENANT_ID }}
+              subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
         - name: setup
           run: bash setup.sh
           working-directory: cli
@@ -125,24 +126,29 @@ Your workflow file is made up of a trigger section and jobs:
           run: bash -x ../../../run-job.sh pipeline.yml
           working-directory: cli/jobs/pipelines/nyc-taxi
     ```
-
+    
 1. Select **View runs**. 
 1. Enable workflows by selecting **I understand my workflows, go ahead and enable them**.
 1. Select the **cli-jobs-pipelines-nyc-taxi-pipeline workflow** and choose to **Enable workflow**. 
+
     :::image type="content" source="media/how-to-github-actions-machine-learning/enable-github-actions-ml-workflow.png" alt-text="Screenshot of enable GitHub Actions workflow.":::
+
 1. Select **Run workflow** and choose the option to **Run workflow** now. 
+
     :::image type="content" source="media/how-to-github-actions-machine-learning/github-actions-run-workflow.png" alt-text="Screenshot of run GitHub Actions workflow.":::
 
- # [OpenID Connect](#tab/openid)
+# [Service principal](#tab/userlevel)
+
 
 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](https://docs.github.com/actions/using-workflows/events-that-trigger-workflows). 
-- In the jobs section of the workflow, you checkout code and log into Azure with the Azure login action using OpenID Connect.
+- 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)](how-to-configure-cli.md). 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.
+1. In your forked repository, open `.github/workflows/cli-jobs-pipelines-nyc-taxi-pipeline.yml` and verify that your workflow looks like this. 
 
     ```yaml
     name: cli-jobs-pipelines-nyc-taxi-pipeline
@@ -166,11 +172,9 @@ Your workflow file is made up of a trigger section and jobs:
         - name: check out repo
           uses: actions/checkout@v2
         - name: azure login
-          uses: azure/login@v1
+          uses: azure/login@v2
           with:
-              client-id: ${{ secrets.AZURE_CLIENT_ID }}
-              tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-              subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
+            creds: ${{secrets.AZURE_CREDENTIALS}}
         - name: setup
           run: bash setup.sh
           working-directory: cli
@@ -179,16 +183,14 @@ Your workflow file is made up of a trigger section and jobs:
           run: bash -x ../../../run-job.sh pipeline.yml
           working-directory: cli/jobs/pipelines/nyc-taxi
     ```
-    
+
 1. Select **View runs**. 
 1. Enable workflows by selecting **I understand my workflows, go ahead and enable them**.
 1. Select the **cli-jobs-pipelines-nyc-taxi-pipeline workflow** and choose to **Enable workflow**. 
-
     :::image type="content" source="media/how-to-github-actions-machine-learning/enable-github-actions-ml-workflow.png" alt-text="Screenshot of enable GitHub Actions workflow.":::
-
 1. Select **Run workflow** and choose the option to **Run workflow** now. 
-
     :::image type="content" source="media/how-to-github-actions-machine-learning/github-actions-run-workflow.png" alt-text="Screenshot of run GitHub Actions workflow.":::
+
 ---
 
 ## Step 6: Verify your workflow run