Evaluation, Testing, and Measurement

If Chapter 1 taught you to get working baselines and Chapter 2 gave you a clean pipe, this chapter is about confidence: knowing what “good” means, measuring it the same way every time, and catching regressions before users do. We’ll keep the loop tight – torchmetrics inside the training run, unit tests with pytest around your data and model code, property-based checks with Hypothesis for tricky invariants, a small golden set that travels with the repo, and a sober look at A/B tests when your changes meet traffic.