Designing Machine Learning Systems: An Engineer's Field Guide to Building ML That Ships, Scales, and Survives Production

The skill that separates senior ML engineers from everyone else is designing the full system around a model. This field guide teaches you to design ML systems that ship, scale, and survive production, and to ace the system design interview.

Most machine learning introductions stop at the model: collect data, train, measure accuracy, declare success. But a model that lives only in a notebook is not an ML system. It is a draft of one. Turning that draft into something that ships, scales under load, and survives when the data changes is a different kind of work.

Designing Machine Learning Systems is the engineer's field guide to that work. Book 2 of the ML Engineer's Journey picks up exactly where the Fundamentals left off and answers the question every working engineer eventually faces: not how to train a model, but how to design the system around it.

Eleven chapters move from models to systems: requirements, constraints, and cost; data system design; feature engineering and feature stores; model development and experimentation; serving architectures for batch, real-time, and streaming workloads; deployment, CI/CD, and release strategy; and monitoring, drift, and reliability. The book then covers ML system design interview patterns, what changes when you build for regulated and mission-critical domains, and two end-to-end designs that pull everything together.

Each chapter closes with a Quick Check and Key Takeaways, and an interview-prep thread runs through the whole book, supported by an ML System Design Interview Field Kit appendix. Tool-neutral and pattern-first, it is written for mid-level ML engineers, software engineers moving into ML, and anyone preparing for a system design interview who wants to design a complete, defensible ML system and defend it in review.

© 2026 PublishDrive (E-bog): 9798904982874

Udgivelsesdato

E-bog: 27. august 2026