About This Book
Most AI projects work in the lab. Few survive contact with production.
AI in Production is a practitioner's guide to the gap between those two states — the decisions, disciplines, and operating practices that determine whether an AI system actually delivers value in the real world.
This book does not cover how to train models. It covers what happens after: how you ship them responsibly, how you govern what they do, how you observe them when things go wrong, and how you scale them without losing control.
What's Inside
Shipping — how to cross the threshold from working prototype to production system with confidence, not hope. Covers readiness criteria, deployment gates, and the minimum viable governance layer you need before going live.
Governing — accountability frameworks for AI decisions, how to assign ownership in regulated environments, and the governance structures that separate auditable AI from a liability.
Observability — what to instrument, what to alert on, and how to build the telemetry that lets you understand what your system is doing in real time, not after a customer complaint.
Scaling — the operational practices that let you grow a production AI system without accumulating technical and governance debt that will eventually break it.
Who This Is For
This book is written for the engineers, architects, and leads who are responsible for AI systems that run in production — not for data scientists building models, but for the people who have to keep them running.
If you are accountable for an AI system and you need it to be reliable, observable, and defensible, this book is for you.
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