dataset formats, ingestion, and validation pipelines

A dataset pipeline is the quiet part of ML work that decides whether a model ever sees trustworthy examples. The builder job is not "save some rows"; it is "make the next run explainable."

In this chapter you practice choosing a format for the job, paging through an API without duplicating data, catching schema drift, ordering pipeline stages, and leaving a manifest someone else can read. The exercises use plain Python lists and dictionaries so the shape is visible in the browser.

By the end, a useful dataset run should tell you what source it read, where it stopped, how many rows passed validation, what was quarantined, and which file or partition is ready for the next step.