Merge pull request #2805 from Blackmist/fresh-370728

freshness

Author: prmerger-automator[bot]

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
 ---
@@ -17,9 +17,9 @@ ms.custom: github-actions-azure
 [!INCLUDE [v2](includes/machine-learning-dev-v2.md)]
 Get started with [GitHub Actions](https://docs.github.com/en/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](./overview-what-is-azure-machine-learning.md). You'll train a [scikit-learn](https://scikit-learn.org/) linear regression model on the NYC Taxi dataset. 
+This article teaches you how to create a GitHub Actions workflow that builds and deploys a machine learning model to [Azure Machine Learning](./overview-what-is-azure-machine-learning.md). You train a [scikit-learn](https://scikit-learn.org/) 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.
+GitHub Actions use 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.
 
 
 ## Prerequisites
@@ -45,7 +45,7 @@ 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 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). 
 
 ### Generate deployment credentials
 
@@ -57,7 +57,7 @@ You'll need to first define how to authenticate with Azure. You can use a [servi
 
 ## Step 3: Update `setup.sh` to connect to your Azure Machine Learning workspace
 
-You'll need to update the CLI setup file variables to match your workspace. 
+You need to update the CLI setup file variables to match your workspace. 
 
 1. In your forked repository, go to `azureml-examples/cli/`. 
 1. Edit `setup.sh` and update these variables in the file. 
@@ -70,7 +70,7 @@ You'll need to update the CLI setup file variables to match your 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. 
+You use a `pipeline.yml` file to deploy your Azure Machine Learning pipeline. The pipeline 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`. 
 1. 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.
@@ -84,7 +84,7 @@ Your workflow authenticates with Azure, sets up the Azure Machine Learning CLI,
 
 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 check out 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.
 
 
@@ -137,7 +137,7 @@ Your workflow file is made up of a trigger section and jobs:
 
 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 check out 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
@@ -193,7 +193,7 @@ Your workflow file is made up of a trigger section and jobs:
 
 ## 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. 
+1. Open your completed workflow run and verify that the build job ran successfully. You see a green checkmark next to the job. 
 1. 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. 
 
     :::image type="content" source="media/how-to-github-actions-machine-learning/github-actions-machine-learning-nyc-taxi-complete.png" alt-text="Screenshot of successful Machine Learning Studio run.":::