The Azure CAT ML team have built the following GitHub Repo which contains code and pipeline definition for a machine learning project demonstrating how to automate an end to end ML/AI workflow.
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 and integration testing.
This reference architecture shows how to implement continuous integration (CI), continuous delivery (CD), and retraining pipeline for an AI application using Azure DevOps and Azure Machine Learning.
The solution example is built on the scikit-learn diabetes dataset but can be easily adapted for any AI scenario and other popular build systems such as Jenkins and Travis.
Data Scientist writes/updates the code and push it to git repo. This triggers the Azure DevOps build pipeline (continuous integration).
Once the Azure DevOps build pipeline is triggered, it runs following types of tasks:
Note: The Publish Azure ML pipeline task currently runs for every code change
The Azure ML Retraining pipeline is triggered once the Azure DevOps build pipeline completes. All the tasks in this pipeline runs on Azure ML Compute created earlier. Following are the tasks in this pipeline:
Evaluate Model task evaluates the performance of newly trained model with the model in production. If the new model performs better than the production model, the following steps are executed. If not, they will be skipped.
Register Model task takes the improved model and registers it with the Azure ML Model registry. This allows us to version control it.
Once you have registered your ML model, you can use Azure ML + Azure DevOps to deploy it.
The Package Model task packages the new model along with the scoring file and its python dependencies into a docker image and pushes it to Azure Container Registry. This image is used to deploy the model as web service.
The Deploy Model task handles deploying your Azure ML model to the cloud (ACI or AKS). This pipeline deploys the model scoring image into Staging/QA and PROD environments.
The second task invokes the web service by calling its REST endpoint with dummy data.
You can find the details of the code and scripts in the repository here
For some background here is video overview of the team sharing the best practice guidance in more detail
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