Introduction to machine learning operations (MLOps): Review

One screenshot shows a generic GitHub interface. The unit itself is just a wall of text explaining what Git is. Then the knowledge check asks me what the two main features of source control are. I didn't need to do any work to get here.

This isn't a course for builders. It's a product catalog for Azure Machine Learning and Azure DevOps. You read about what a CI/CD pipeline is, and then you're shown a diagram of how Azure's services connect to form one. There is no sandbox, no repository to clone, no YAML file to edit. You don't build anything. The entire path is a guided tour of marketing pages, stitched together with multiple-choice questions that a manager could guess.

I went in expecting a lightweight tutorial—maybe setting up a simple GitHub Action to trigger an Azure ML job. Something tangible. Instead, I got definitions. They explain that 'technical debt' is bad and that 'automation' is good. Thanks. I get that this is an 'introduction,' but it's an introduction to a brochure, not an introduction to the practice of MLOps.

I clicked through the whole thing in about an hour and a half while waiting for a real model to train. It's fine for learning the names of the Azure tools your organization just bought, but that's about it. You won't leave with a single line of code or a working pipeline.