Introduction to cost management for AI workloads: Review

It's a little strange to take a course on cost management that is itself completely free. I clicked through this in about 25 minutes. The whole thing is a text-and-diagrams module, a well-organized blog post disguised as a course. There are no labs, no sandboxes, not even a knowledge check that makes you think. You just read and click 'Continue'.

The content itself is okay for what it is. It introduces the idea of FinOps and correctly points out that your architectural choices—like using a serverless function versus a provisioned endpoint—are the real cost drivers. The diagrams showing how costs accumulate from data ingestion to model training to inference were actually quite helpful. It gave me a mental map for where my (future) money would go. They break down the difference between pricing for services like Azure OpenAI (token-based) and Azure ML (compute-hour-based), which is a critical distinction.

But that's where it stops. It tells you *what* to think about, but not *how*. It's a vocabulary list. You'll learn what 'cost allocation' and 'anomaly detection' mean in a FinOps context, but you won't actually do any of it. This is a good pre-reading assignment before you're let loose on a real Azure account with a credit card attached. For a student, it's a zero-risk way to learn the jargon before an internship interview. I just wish it had one tiny, interactive exercise.