The Enterprise MLOps Playbook

How to Deploy, Govern, and Scale Machine Learning Systems in the Real World

By: Jazper Carter

Narrated by: Virtual Voice

This book was written to support both Managers and Technical readers with its "Geeks Only" sections in each chapter that include agnostic pseudocode. It’s over 450 pages of MLOps experience...

Fewer than one in five trained machine learning models ever reaches production. The rest expire in data science notebooks, stall in handoff meetings, pilot successfully and quietly disappear when no team owns the deployment path, or reach production and degrade silently while every dashboard reports green. At enterprise scale, this gap costs tens of millions in failed experiments, accumulates regulatory exposure with every unaudited model decision, and compounds into a credibility deficit that makes the next budget cycle harder to win. The gap is not a model quality problem. The models are good enough. It is an operational problem — and closing it requires the engineering discipline, platform architecture, and organizational design that transform machine learning from an expensive experiment into a governed, scalable business capability.
Inside this book, readers will learn how to:

• Build a production-grade ML platform with compute orchestration, feature stores, model registries, and integrated serving layers as first-class architectural components — not disconnected tools assembled under deadline pressure
• Close the deployment gap with CI/CD pipelines designed for ML — including automated training, evaluation, schema validation, canary releases, shadow deployments, and rollback mechanisms that catch model regressions before they reach production traffic
• Monitor production model behavior with the signals that matter — data drift detection, training-serving skew measurement, prediction confidence degradation, and SLO-based alerting that surfaces silent failures before they become operational incidents
• Operate large language models, autonomous AI agents, and multi-modal pipelines at production scale — managing prompt artifact versioning, tool invocation traces, temporal signal alignment across modalities, and inference cost structures fundamentally different from traditional supervised models
• Embed governance and compliance into platform architecture — not policy documents — with audit trails, model cards, bias measurement pipelines, fairness evaluation gates, and data lineage chains that satisfy NIST AI RMF and EU AI Act requirements by design
• Control ML infrastructure cost with FinOps discipline — GPU utilization attribution, cloud burst economics, per-model cost tracking, and autoscaling strategies that connect infrastructure investment to deployed model business value
• Design the MLOps organization with clear role boundaries, shared accountability for production models, and the platform-as-product mindset that allows a small platform team to support a growing portfolio without proportional operational growth
• Scale from a handful of production models to hundreds using platform standardization, multi-tenancy, lifecycle automation, and a five-dimension maturity framework that sequences investment in the correct order

Each chapter includes a Geeks Only section with architecture patterns, pseudocode, and implementation detail, and a Manager's Decision Guide with the investment frames, signals, and questions that separate effective ML programs from expensive ones. The book is technology-agnostic and vendor-neutral. The patterns, frameworks, and decision guides apply across any technology stack and survive every platform refresh cycle.
In production ML, the model is one component. The operational system that surrounds it is what determines whether the investment pays.