ML.NET Model Lifecycle with Azure DevOps CI/CD pipelines Cesar de la Torre

When you infuse AI (such as an ML.NET model) into your application, then your application lifecycle needs to be extended so it additionally embraces the'Machine Learning Model Lifecycle'. When deploying ML models to production, you need to automate the process to track, version, audit, certify and re-use every asset in your ML model lifecycle along with the end-user application lifecycle. In short, the ML model lifecycle process must be part of the application's Continuous Integration (CI) and Continuous Delivery (CD) pipelines. Let's walk through the diagram above to understand how this integration between the ML model lifecycle and the app development lifecycle can be achieved. For this common scenario, a starting assumption is that Git is used as your code repository, but it could be any other source code management platform.