DevOps for AI template will help you to understand how to build the Continuous Integration and Continuous Delivery pipeline for a ML/AI project. We will be using the Azure DevOps Project for build and release pipelines along with Azure ML services for ML/AI model management and operationalization.

This template contains code and pipeline definition for a machine learning project demonstrating how to automate the end to end ML/AI project. The build pipelines include DevOps tasks for data sanity test, unit test, model training on different compute targets, model version management, model evaluation/model selection, model deployment as realtime web service, staged deployment to QA/prod, integration testing and functional testing.

• Active Azure subscription
• Minimum contributor access to Azure subscription

Skip above step if already done.

Once the template is imported for personal Azure DevOps account using DevOps demo generator, you need to follow below steps to get the pipeline running:

1. Go to the Pipelines -> Builds on the newly created project and click Edit on top right
2. Click on Create or Get Workspace task, select the Azure subscription where you want to deploy and run the solution, and click Authorize
3. Click all other tasks below it and select the same subscription (no need to authorize again)
4. Once the tasks are updated with subscription, click on Save & queue and select Save

1. Go to the Pipelines -> Releases and click Edit on top
   
2. Click on 1 job, 4 tasks to open the tasks in QA stage
3. Update the subscription details in two tasks
4. Click on Tasks on the top to switch to the Prod stage, update the subscription details for the two tasks in prod
5. Once you fix all the missing subscription, the Save is no longer grayed, click on save to save the changes in release pepeline

1. Go to the Repos on the newly created Azure DevOps project
2. Open the config file /aml_config/config.json and edit it
3. Put your Azure subscription ID in place of <>
4. Change resource group and AML workspace name if you want
5. Put the location where you want to deploy your Azure ML service workspace
6. Save the changes and commit these changes to master branch
7. The commit will trigger the build pipeline to run deploying AML end to end solution
8. Go to Pipelines -> Builds to see the pipeline run

1. Prepare the python environment
2. Get or Create the workspace
3. Submit Training job on the remote DSVM / Local Python Env
4. Register model to workspace
5. Create Docker Image for Scoring Webservice
6. Copy and Publish the Artifacts to Release Pipeline

In Release pipeline we deploy the image created from the build pipeline to Azure Container Instance and Azure Kubernetes Services

1. Prepare the python environment
2. Create ACI and Deploy webservice image created in Build Pipeline
3. Test the scoring image

1. Prepare the python environment
2. Deploy on AKS

   • Create AKS and create a new webservice on AKS with the scoring docker image

     OR

   • Get the existing AKS and update the webservice with new image created in Build Pipeline

3. Test the scoring image

You can find the details of the code ans scripts in the repository here

• Azure Machine Learning(Azure ML) Service Workspace

• Azure ML Samples

• Azure ML Python SDK Quickstart

• Azure DevOps