商品簡介
This book explores how senior machine learning engineers think, make decisions, and design real-world ML systems beyond models and algorithms. It focuses on mental models, tradeoffs, and failure modes that only appear in production, such as data drift, feedback loops, system reliability, monitoring, and ethical responsibility. Rather than teaching how to train models, the book explains how to frame problems correctly, evaluate impact, manage risk, and decide when machine learning should-or should not-be used. It is a practical guide to judgment, system thinking, and responsibility in modern machine learning engineering.
