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Intial setup for Azure DevOps

This tutorial covers:

  • Creating an Azure Machine Learning workspace in Azure
  • Setup on an end-to-end demo pipeline in Azure DevOps

Fork the repo

Before you get started, fork this repository into your own GitHub account:

This will allow you to experiment and make changes to it.

Create an Azure Machine Learning workspace

Next, create a new Machine Learning Workspace by clicking here. This link brings you directly to the creation page in the Azure Portal:

This repo uses aml-demo as the workspace and reosurce group name. Feel free to pick a region of your choice and select Enterprise Edition (Basic should work too, but has not been tested). CLick Review + Create and finally Create.

Create and setup a new project in Azure DevOps

Navigate to Azure DevOps and sign-in. In case you are using Azure DevOps for the very first time, you might be asked to create a new organization. Feel free to choose any name:

Once created, you can create a new project and give it a name:

Next, you need to connect Azure DevOps to your Azure Subscription, to be more precise, to your Machine Learning workspace's resource group. For this, go to the Project Settings and select Service Connections:

From there, create a new Service Connection of type Azure Resource Manager:

Note: In case your UI looks different, scroll down two images

Lastly, give it a name (this repo uses azmldemows as default), and enter the details of your Resource

Note: If you want to limit Azure DevOps's access to only the Machine Learning workspace, you can install the Azure DevOps Machine Learning Extension to your project. In this case, you'll be able to select the Machine Learning workspace.

Note: In some cases, your Azure DevOps UI might look a bit different:

In this case, you can point it directly to your newly created Machine Learning workspace:

Import existing YAML pipeline to Azure DevOps

Next, select Pipelines and create a new pipeline. Select that you want to access code from GitHub:

Note: At this point, you might need to link your GitHub account to Azure DevOps (not shown here).

Select the repository fork you've forked earlier (this should show as <your github username>/pipelines-azureml):

Next, let's point the new pipeline to an existing pipeline YAML in your GitHub repo:

Lastly, we need to select from where the pipeline definition should come from. In this case, point it to pipelines/diabetes-train-and-deploy.yml:

In the next step, you can review the pipeline. In case your Service connection or Machine Learning workspace, etc. are named differently, you can update the variables here:

Once the configuration looks right, give it a test run:

By clicking on the pipline job, you can retrieve more details:

That's it! Your first model should be training by now! 👍