A model is only as good as the data flowing into it, so the pipeline that produces that data has to be reliable in ways an analytics pipeline does not.

When a normal analytics pipeline fails, a dashboard shows up late and someone notices. When an ML data pipeline fails silently, the model keeps serving predictions on stale, missing, or corrupted features. Nothing throws an error, the dashboards look fine, and product quality drops while every health check stays green.

This chapter focuses on the data pipelines that feed feature stores, training datasets, evaluation jobs, and production models.