Production ML Case Studies: Reading Guide
Build reproducible scoring pipelines, validate explanations, and migrate the surrounding platform safely.
Reading order
Version data and transforms, construct deterministic training samples, validate artifacts, and publish distributed predictions without partial or duplicate results.
Reconstruct a score from its baseline and contributions, preserve output units, validate background choices, and design an efficient distributed explanation pipeline.
Inventory dependencies, preserve datasets and artifacts, replay pipelines, and validate a staged cutover with a tested rollback path.
How to use these notes
Start with the worked example in each chapter. Calculate the answer before changing the interactive figure, then use the self-check to test the distinction the chapter teaches. The numerical examples are deliberately small enough to audit by hand; they are teaching examples, not production benchmarks.
Follow the prerequisites when a concept is unfamiliar. Related notes link to existing foundations so each chapter can concentrate on its own question.