What is MLOps?

MLOps empowers data scientists and app developers to help bring ML models to production. MLOps enables you to track / version / audit / certify / re-use every asset in your ML lifecycle and provides orchestration services to streamline managing this lifecycle.

How does Azure ML help with MLOps?

Azure ML contains a number of asset management and orchestration services to help you manage the lifecycle of your model training & deployment workflows.

With Azure ML + Azure DevOps you can effectively and cohesively manage your datasets, experiments, models, and ML-infused applications.

MLOps Best Practices

We recommend the following steps in your CI process:

• Train Model - run training code / algo & output a model file which is stored in the run history.

• Evaluate Model - compare 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 - take the best model and register it with the Azure ML Model registry. This allows us to version control it.

You will learn Machine Learning Automation using Azure DevOps. Here are the below topics related to the ML.

• Code Analysis on SonarQube

• Training the Model

• Evaluation of Production Model with Newly Trained Model

• Register Model

• Create Docker Scoring Image

• Build and Release Pipeline

• Deploy on Azure Container Instance

• Test Azure Container Instance Webservice

Finally input sample data to consume the Webservice.