A practical guide for product managers working with ML teams. Covers the ML product lifecycle, how to frame problems as ML problems, data requirements, evaluation metrics, and common pitfalls (data leakage, overfitting, bias). Teaches PMs enough to be dangerous without requiring deep technical knowledge.