Operationalize machine learning models (MLOps): Review

The GitHub Actions part is what got me here. I've stitched together enough half-baked deployment scripts to know the pain. This path gives you the happy-path version of an Azure ML workflow, complete with a temporary sandbox so you don't need to fiddle with your own subscription.

The labs are mostly copy-pasting YAML and Python from the instructions into the terminal. It works. You see a pipeline run, you see a model get registered. It's a bit like a paint-by-numbers exercise, but I’ll admit it’s a useful way to see all the components interact for the first time. The final module on deploying the model was the most concrete part. I spent a Saturday clicking through it all. It didn't make me an MLOps guru, but now I know what the Azure console buttons are actually for.