"Model Governance at Scale: Registries, Approvals, and Lifecycle Controls"
Model governance fails most often not from lack of intent, but from weak mechanics: unclear decision rights, unenforced standards, and registries that devolve into artifact dumps. This book is written for senior ML engineers, MLOps/platform leaders, risk and compliance partners, and technical managers who need governance that scales across dozens to thousands of models—without turning delivery into bureaucracy.
You'll learn to treat governance as an operating system for the model lifecycle: translating policy into checkable control objectives, calibrating rigor with risk tiering, and designing a human-and-automation operating model with real separation of duties. The core of the book shows how to make the model registry a true system of record—defining entities, lifecycle states, required metadata, promotion semantics, and tamper-evident auditability. From there it goes deep on provenance, versioning, and lineage; evidence-backed approval workflows and promotion gates; and production controls that tie deployment, monitoring, change management, incidents, and retirement back to approvals and traceability.
Expect practical patterns, decision criteria, and operational flows you can implement in existing tooling—especially where trade-offs, exceptions, and "break-glass" paths determine whether governance actually works in production.
