Merge pull request #212876 from juliakm/users/jukullam/port-machine-learning-azdevops

Move DevOps ML article and add ref to text repo

Author: 19BMG00

.openpublishing.publish.config.json

@@ -955,6 +955,12 @@
       "url": "https://github.com/Azure-Samples/cosmos-db-mongodb-api-dotnet-samples",
       "branch": "main",
       "branch_mapping": {}
+    },
+    {
+      "path_to_root": "reusable-content",
+      "url": "https://github.com/MicrosoftDocs/reusable-content",
+      "branch": "main",
+      "branch_mapping": {}
     }
   ],
   "branch_target_mapping": {

articles/machine-learning/how-to-devops-machine-learning.md

@@ -0,0 +1,151 @@
+---
+title: Azure DevOps for CI/CD
+titleSuffix: Azure Machine Learning
+description: Use Azure Pipelines for flexible MLOps automation
+services: machine-learning
+ms.service: machine-learning
+ms.subservice: mlops
+author: juliakm
+ms.author: jukullam
+ms.date: 09/28/2022
+ms.topic: how-to
+ms.custom: devops-pipelines-deploy
+---
+
+# Use Azure Pipelines with Azure Machine Learning
+
+**Azure DevOps Services | Azure DevOps Server 2022 - Azure DevOps Server 2019**
+
+You can use an [Azure DevOps pipeline](/azure/devops/pipelines/) to automate the machine learning lifecycle. Some of the operations you can automate are:
+
+* Data preparation (extract, transform, load operations)
+* Training machine learning models with on-demand scale-out and scale-up
+* Deployment of machine learning models as public or private web services
+* Monitoring deployed machine learning models (such as for performance or data-drift analysis)
+
+This article will teach you how to create an Azure Pipeline 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 linear regression model on the Diabetes dataset.
+
+This tutorial uses [Azure Machine Learning Python SDK v2](/python/api/overview/azure/ml/installv2) and [Azure CLI ML extension v2](/cli/azure/ml). 
+
+## Prerequisites
+
+Complete the [Quickstart: Get started with Azure Machine Learning](quickstart-create-resources.md) to:
+* Create a workspace
+* Create a cloud-based compute instance to use for your development environment
+* Create a cloud-based compute cluster to use for training your model
+
+## Step 1: Get the code
+
+Fork the following repo at GitHub:
+
+```
+https://github.com/azure/azureml-examples
+```
+
+## Step 2: Sign in to Azure Pipelines
+
+[!INCLUDE [include](~/reusable-content/devops-pipelines/sign-in-azure-pipelines.md)]
+
+[!INCLUDE [include](~/reusable-content/devops-pipelines/create-project.md)]
+
+## Step 3: Create an Azure Resource Manager connection
+
+You'll need an Azure Resource Manager connection to authenticate with Azure portal. 
+
+1. In Azure DevOps, open the **Service connections** page.
+
+1. Choose **+ New service connection** and select **Azure Resource Manager**.
+
+1. Select the default authentication method, **Service principal (automatic)**.
+
+1. Create your service connection. Set your subscription, resource group, and connection name. 
+
+    :::image type="content" source="media/how-to-devops-machine-learning/machine-learning-arm-connection.png" alt-text="Screenshot of ARM service connection.":::
+
+
+## Step 4: Create a pipeline
+
+1. Go to **Pipelines**, and then select **New pipeline**.
+
+1. Do the steps of the wizard by first selecting **GitHub** as the location of your source code.
+
+1. You might be redirected to GitHub to sign in. If so, enter your GitHub credentials.
+
+1. When you see the list of repositories, select your repository.
+
+1. You might be redirected to GitHub to install the Azure Pipelines app. If so, select **Approve & install**.
+
+1. Select the **Starter pipeline**. You'll update the starter pipeline template.
+
+## Step 5: Create variables
+
+You should already have a resource group in Azure with [Azure Machine Learning](overview-what-is-azure-machine-learning.md). To deploy your DevOps pipeline to AzureML, you'll need to create variables for your subscription ID, resource group, and machine learning workspace. 
+
+1. Select the Variables tab on your pipeline edit page.  
+
+    :::image type="content" source="media/how-to-devops-machine-learning/machine-learning-select-variables.png" alt-text="Screenshot of variables option in pipeline edit. ":::   
+ 
+1. Create a new variable, `Subscription_ID`, and select the checkbox **Keep this value secret**. Set the value to your [Azure portal subscription ID](/azure/azure-portal/get-subscription-tenant-id).
+1. Create a new variable for `Resource_Group` with the name of the resource group for Azure Machine Learning (example: `machinelearning`). 
+1. Create a new variable for `AzureML_Workspace_Name` with the name of your Azure ML workspace (example: `docs-ws`).
+1. Select **Save** to save your variables. 
+
+## Step 6: Build your YAML pipeline
+
+Delete the starter pipeline and replace it with the following YAML code. In this pipeline, you'll:
+
+* Use the Python version task to set up Python 3.8 and install the SDK requirements.
+* Use the Bash task to run bash scripts for the Azure Machine Learning SDK and CLI.
+* Use the Azure CLI task to pass the values of your three variables and use papermill to run your Jupyter notebook and push output to AzureML. 
+
+```yaml
+trigger:
+- main
+
+pool:
+  vmImage: ubuntu-latest
+
+steps:
+- task: UsePythonVersion@0
+  inputs:
+    versionSpec: '3.8'
+- script: pip install -r sdk/dev-requirements.txt
+  displayName: 'pip install notebook reqs'
+- task: Bash@3
+  inputs:
+    filePath: 'sdk/setup.sh'
+  displayName: 'set up sdk'
+
+- task: Bash@3
+  inputs:
+    filePath: 'cli/setup.sh'
+  displayName: 'set up CLI'
+
+- task: AzureCLI@2
+  inputs:
+    azureSubscription: 'your-azure-subscription'
+    scriptType: 'bash'
+    scriptLocation: 'inlineScript'
+    inlineScript: |
+           sed -i -e "s/<SUBSCRIPTION_ID>/$(SUBSCRIPTION_ID)/g" sklearn-diabetes.ipynb
+           sed -i -e "s/<RESOURCE_GROUP>/$(RESOURCE_GROUP)/g" sklearn-diabetes.ipynb
+           sed -i -e "s/<AML_WORKSPACE_NAME>/$(AZUREML_WORKSPACE_NAME)/g" sklearn-diabetes.ipynb
+           sed -i -e "s/DefaultAzureCredential/AzureCliCredential/g" sklearn-diabetes.ipynb
+           papermill -k python sklearn-diabetes.ipynb sklearn-diabetes.output.ipynb
+    workingDirectory: 'sdk/jobs/single-step/scikit-learn/diabetes'
+```
+
+
+## Step 7: Verify your pipeline run
+
+1. Open your completed pipeline run and view the AzureCLI task. Check the task view to verify that the output task finished running. 
+ 
+   :::image type="content" source="media/how-to-devops-machine-learning/machine-learning-azurecli-output.png" alt-text="Screenshot of machine learning output to AzureML.":::
+
+1. Open Azure Machine Learning studio and navigate to the completed `sklearn-diabetes-example` job. On the **Metrics** tab, you should see the training results. 
+
+    :::image type="content" source="media/how-to-devops-machine-learning/machine-learning-training-results.png" alt-text="Screenshot of training results.":::
+
+## Clean up resources
+
+If you're not going to continue to use your pipeline, delete your Azure DevOps project. In Azure portal, delete your resource group and Azure Machine Learning instance.

articles/machine-learning/media/how-to-devops-machine-learning/machine-learning-arm-connection.png

@@ -607,7 +607,7 @@
           items: 
             - name: Azure Pipelines for CI/CD
               displayName: continuous, integration, delivery
-              href: /azure/devops/pipelines/targets/azure-machine-learning?context=azure/machine-learning/context/ml-context
+              href: how-to-devops-machine-learning.md
             - name: GitHub Actions for CI/CD
               href: how-to-github-actions-machine-learning.md
             - name: Create event-driven workflows