Machine Learning System Design 2025

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Machine Learning System Design: With end-to-end examples

Year of Publication: 2025
Author: Babushkin Valerii , Kravchenko Arseny
Publishing House: Manning Publications Co.
ISBN: 978-1-6334-3875-0
Language: English

Description:
Get the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems.

From information gathering to release and maintenance, Machine Learning System Design guides you step-by-step through every stage of the machine learning process. Inside, you’ll find a reliable framework for building, maintaining, and improving machine learning systems at any scale or complexity.

In Machine Learning System Design: With end-to-end examples you will learn:
• The big picture of machine learning system design
• Analyzing a problem space to identify the optimal ML solution
• Ace ML system design interviews
• Selecting appropriate metrics and evaluation criteria
• Prioritizing tasks at different stages of ML system design
• Solving dataset-related problems with data gathering, error analysis, and feature engineering
• Recognizing common pitfalls in ML system development
• Designing ML systems to be lean, maintainable, and extensible over time
Authors Valeri Babushkin and Arseny Kravchenko have filled this unique handbook with campfire stories and personal tips from their own extensive careers. You’ll learn directly from their experience as you consider every facet of a machine learning system, from requirements gathering and data sourcing to deployment and management of the finished system.

About the technology:
Designing and delivering a machine learning system is an intricate multistep process that requires many skills and roles. Whether you’re an engineer adding Machine Learning to an existing application or designing a ML system from the ground up, you need to navigate massive datasets and streams, lock down testing and deployment requirements, and master the unique complexities of putting ML models into production. That’s where this book comes in.

About the book:
Machine Learning System Design shows you how to design and deploy a machine learning project from start to finish. You’ll follow a step-by-step framework for designing, implementing, releasing, and maintaining ML systems. As you go, requirement checklists and real-world examples help you prepare to deliver and optimize your own ML systems. You’ll especially love the campfire stories and personal tips, and ML system design interview tips.

Machine Learning System Design is a comprehensive step-by-step guide designed to help you work on your ML system at every stage of its creation—from gathering information and taking preliminary steps to implementation, release, and ongoing maintenance.

As the title suggests, the book is dedicated to ML system design, not focusing on a particular technology but rather providing a high-level framework on how to approach problems related to building, maintaining, and improving ML systems of various scales and levels of complexity.

As ML and AI are getting bigger and bigger these days, there are many books and courses on algorithms, domains, and other specific aspects. However, they don’t provide an entire vision. This leads to the problem Arseny and Valerii have seen in multiple companies, where solid engineers successfully build scattered subcomponents that can’t be combined into a fully functioning, reliable system. This book aims to, among other things, fill this gap.

This book is not beginner friendly. We expect our readers to be familiar with ML basics (you can understand an ML textbook for undergraduate students) and to be fluent in applied programming (you have faced real programming challenges outside the studying sandbox).

What's inside:
• Metrics and evaluation criteria
• Solve common dataset problems
• Common pitfalls in ML system development
• ML system design interview tips

About the reader:
For readers who know the basics of software engineering and Machine Learning. Examples in Python.

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