About this book:

"MLOps in Production" provides a comprehensive guide to implementing end-to-end practices for reliable machine learning delivery. The book emphasizes that machine learning only creates value when models are consistently healthy and perform reliably in production, addressing the gap between experimental prototypes and durable products. It introduces MLOps as an operational framework integrating data, code, and infrastructure to ensure repeatable deployment, monitoring, and governance of ML pipelines. The target audience includes data scientists, ML engineers, platform teams, and managers seeking to reduce deployment risk and accelerate delivery.

The book details 25 chapters covering the entire ML lifecycle. It begins with MLOps fundamentals, outlining principles like automation, reproducibility across data, code, and models, and the importance of continuous integration and delivery (CI/CD). Chapters delve into architectural components of the ML production stack, emphasizing data management, lineage, and governance, as well as the critical role of feature engineering and feature stores in preventing training-serving skew. Testing ML systems, from unit and integration tests to model validation, is thoroughly discussed, leading into automated builds, checks, and continuous delivery patterns like blue/green, canary, and shadow deployments.

Further sections focus on operationalizing ML, including packaging and artifact management with containers and model registries, and various model serving patterns (batch, real-time, streaming). The book highlights the importance of orchestrating pipelines with DAGs and schedulers, and establishing robust observability through metrics, logs, traces, and Service Level Objectives (SLOs). Crucially, it addresses detecting and responding to data and concept drift through automated retraining and continuous learning loops. Advanced topics cover evaluation in production via A/B tests and champion-challenger experiments, continuous monitoring of fairness, bias, and performance, and reliability engineering principles like resilience, high availability, and safe rollbacks.

Finally, the book extends to broader organizational and strategic considerations. It covers security and privacy for ML systems, cost and capacity management for ML workloads, and establishing incident response, runbooks, and on-call procedures. The importance of governance, compliance, and risk management is stressed for responsible AI delivery. The book concludes by discussing platform patterns (build vs. buy and tooling integration), organizational design and collaboration for MLOps, and charting maturity roadmaps and future directions for the evolving field of machine learning operations.