MLOps : Engineering Machine Learning Systems

MLOps: Engineering Machine Learning Systems by Sandeep Vattikuti is a practitioner's textbook built on one central observation: a machine learning model is a small artefact surrounded by a large system, and almost all of the engineering effort, and almost all of the failure, lives in that surrounding system rather than in the model itself. Written for data engineers, ML engineers, platform engineers and technical leads, the book treats MLOps as systems engineering rather than a tool or a job title, and organises its ten chapters around the ways production ML systems break and the structures that prevent them. It opens by explaining why ML systems differ from conventional software: their behaviour is specified by data rather than logic, their inputs come from a world nobody controls, and they fail silently instead of loudly, which is why a model can degrade for months before anyone notices. From there it follows the lifecycle as a chain of independently failing subsystems. The data platform chapter covers lakehouse architecture, batch and streaming pipelines, idempotent and replayable pipeline design, and data contracts that turn silent upstream changes into visible failures. The feature chapter tackles training–serving skew and point-in-time correctness, the two problems that motivate feature stores, and explains when building or adopting one is justified. The training chapter argues that an experiment nobody can reproduce is an anecdote and a model nobody can rebuild is a liability, covering experiment tracking, temporal splits, slice analysis, calibration and retraining triggers. Later chapters address packaging and model registries, release strategies such as shadow, canary and blue–green deployment, and rollback as a first-class capability; serving as a queueing and capacity-planning problem governed by latency budgets, batching, model optimisation and unit economics; and monitoring, which the author calls the chapter whose absence causes the most damage, with a four-layer model spanning infrastructure, data, model and business signals, statistical drift tests, the label-delay problem, alert design and incident response. The book then turns to the automation and organisation that keep all of this running, including CI/CD for models, an ML testing pyramid, orchestration, infrastructure as code, internal platform design and team topologies, followed by security, privacy, regulation, fairness, explainability and audit trails. A final chapter on LLMOps identifies what genuinely changes with large language models, from externally owned models and label-free evaluation to per-token cost and retrieval-augmented generation, before consolidating the whole book into a single integrated framework. Each chapter includes learning objectives, worked quantitative examples, original diagrams, a composite case study, key takeaways, and review, numerical, design and research problems, while six appendices supply reference architectures, a metrics and SLO reference, a readiness checklist, operational runbooks, a glossary and a guide to further reading. Throughout, the author holds to two conventions that shape the book's character: every figure is illustrative rather than a measured result from a real organisation, and tooling is discussed by category and capability rather than by brand, so readers learn what a system must guarantee and can evaluate any implementation of it long after specific products have changed.