Performance Reporting

Buildable now. The repo, the spec and the deployment steps are live — the written walkthrough for this one is still being drafted.

A weekly model performance report combining CloudWatch metrics, live evaluation, and the MLflow baseline from project 5 (a --dry-run mode works without AWS credentials). A model nobody is watching is a model nobody can defend in the next incident review — this project is the artifact you point to when someone asks “is it still working?”

Translating CloudWatch metrics and live evaluation into a weekly report is exactly the communication skill that separates a senior ML Engineer from someone who can only train models in isolation.

Clone this project, run it locally, and compare your output against the description above.

Performance Reporting on GitHub ↗

The quiz isn't written yet

The project itself is ready to build — the repo, the spec and the deployment steps are all live. What's missing is the written quiz that goes with it, and the order those get written in is decided by which ones people actually ask for.

The assignment isn't written yet

The project itself is ready to build — the repo, the spec and the deployment steps are all live. What's missing is the written assignment that goes with it, and the order those get written in is decided by which ones people actually ask for.