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Machine Learning System Design: With end-to-end examples
Get the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems.
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 through 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
Machine Learning System Design: With end-to-end examples is a practical guide for planning and designing successful ML applications. It lays out a clear, repeatable framework for building, maintaining, and improving systems at any scale. Authors Arseny Kravchenko and Valeri Babushkin 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
Machine learning system design is complex. The successful ML engineer needs to navigate a multistep process that demands skills from many different fields and roles. This one-of-kind-guide starts by showing you the big picture and then guides you step by step through a framework for creating successful systems. You’ll learn to excel at delivering for global objectives, diving locally into tools, and combining your knowledge into an integrated vision.
about the book
In Machine Learning System Design: With end-to-end examples you’ll find a step-by-step framework for creating, implementing, releasing, and maintaining your ML system. Every part of the life cycle is covered, from information gathering to keeping your system well-serviced. Each stage includes its own handy checklist of requirements and is fully illustrated with real-world examples, including interesting anecdotes from the author’s own careers.
You’ll follow two example companies each building a new ML system, exploring how their needs are expressed in design documents and learning best practices by writing your own. Along the way, you’ll learn how to ace ML system design interviews, even at highly competitive FAANG-like companies, and improve existing ML systems by identifying bottlenecks and optimizing system performance.
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 through 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
Machine Learning System Design: With end-to-end examples is a practical guide for planning and designing successful ML applications. It lays out a clear, repeatable framework for building, maintaining, and improving systems at any scale. Authors Arseny Kravchenko and Valeri Babushkin 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
Machine learning system design is complex. The successful ML engineer needs to navigate a multistep process that demands skills from many different fields and roles. This one-of-kind-guide starts by showing you the big picture and then guides you step by step through a framework for creating successful systems. You’ll learn to excel at delivering for global objectives, diving locally into tools, and combining your knowledge into an integrated vision.
about the book
In Machine Learning System Design: With end-to-end examples you’ll find a step-by-step framework for creating, implementing, releasing, and maintaining your ML system. Every part of the life cycle is covered, from information gathering to keeping your system well-serviced. Each stage includes its own handy checklist of requirements and is fully illustrated with real-world examples, including interesting anecdotes from the author’s own careers.
You’ll follow two example companies each building a new ML system, exploring how their needs are expressed in design documents and learning best practices by writing your own. Along the way, you’ll learn how to ace ML system design interviews, even at highly competitive FAANG-like companies, and improve existing ML systems by identifying bottlenecks and optimizing system performance.
- GenresTechnologyTechnical
325 pages, Hardcover
Published June 1, 2024
13 people are currently reading
64 people want to read
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Displaying 1 - 6 of 6 reviews
March 18, 2025
I recently finished reading Machine Learning System Design: With End-to-End Examples, and it has truly transformed my approach to building and optimizing machine learning pipelines. As someone who's been working with ML systems for a while, I found this book to be an incredibly practical and insightful resource.
The author does a fantastic job of breaking down complex concepts and showcasing real-world problems that we, as practitioners, often encounter. The book is filled with hands-on examples that directly address the kind of issues faced in everyday ML systems, making it immediately relevant. From error analysis to monitoring techniques, these aspects are deeply explored and offered as clear, actionable solutions. It was especially helpful to see how these strategies could be implemented in a way that actually improves the system’s efficiency and accuracy.
One of the standout features of this book is how the author walks through entire end-to-end examples, making it easy to apply the concepts directly to your own work. It really helped me rethink and redesign my ML pipeline strategy. The practical solutions shared in the book gave me new ideas for improving error detection, monitoring, and handling edge cases, which has made my system more robust and reliable.
Overall, this book is a must-read for anyone looking to build or refine their machine learning systems. It's not just theory; it's packed with real-world scenarios that make a difference in everyday operations. Highly recommended!
The author does a fantastic job of breaking down complex concepts and showcasing real-world problems that we, as practitioners, often encounter. The book is filled with hands-on examples that directly address the kind of issues faced in everyday ML systems, making it immediately relevant. From error analysis to monitoring techniques, these aspects are deeply explored and offered as clear, actionable solutions. It was especially helpful to see how these strategies could be implemented in a way that actually improves the system’s efficiency and accuracy.
One of the standout features of this book is how the author walks through entire end-to-end examples, making it easy to apply the concepts directly to your own work. It really helped me rethink and redesign my ML pipeline strategy. The practical solutions shared in the book gave me new ideas for improving error detection, monitoring, and handling edge cases, which has made my system more robust and reliable.
Overall, this book is a must-read for anyone looking to build or refine their machine learning systems. It's not just theory; it's packed with real-world scenarios that make a difference in everyday operations. Highly recommended!
February 25, 2025
I have just finished reading this excellent book by Valerii Babushkin and Arseny Kravchenko, my colleagues from Manning Publishers. I want to share my recommendation with you.
This book will take you on a journey from the initial research, planning, and design phases of machine learning (ML) through all the stages of ML actualization to ML deployment and monitoring. It is replete with end-to-end examples drawn from the author's vast and detailed experience and, as such, contains genuinely actionable examples and advice. If you are involved with any type of ML design and/or development and/or deployment, this book will help you not only achieve your aims but achieve them with confidence and with world-class, robust results going forward.
This book will take you on a journey from the initial research, planning, and design phases of machine learning (ML) through all the stages of ML actualization to ML deployment and monitoring. It is replete with end-to-end examples drawn from the author's vast and detailed experience and, as such, contains genuinely actionable examples and advice. If you are involved with any type of ML design and/or development and/or deployment, this book will help you not only achieve your aims but achieve them with confidence and with world-class, robust results going forward.
February 26, 2025
Machine Learning System Design: With End-to-End Examples is a highly practical, accessible guide that provides readers with the tools and knowledge to design and build machine learning systems from scratch. By covering the entire lifecycle of an ML system, from data collection to deployment, it offers a clear and comprehensive roadmap for creating scalable and efficient ML solutions. While it may not delve deeply into advanced theoretical concepts, its emphasis on practical, hands-on learning makes it an invaluable resource for anyone looking to apply machine learning to real-world problems.
March 7, 2025
A great book that builds the foundation of machine learning system design through comprehensive documentation patterns. It excels by diving into crucial technical details that make a real difference in production, balanced with engaging fireside stories and real-world experiences. Particularly valuable for ML engineers transitioning from modelling to systems architecture.
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March 1, 2025Having a background with traditional software design and development, there was a gap in understanding the ML based System design. Reading this book offered a fresh perspective on how to approach ML system design.
March 2, 2025
The book is readable and convincing as the authors share their practical experiences, serving as examples for different stages in ML development. I appreciate that the book is not a theoretical and dry exposition of principles but explains the steps logically. For me, it was worth reading.
Displaying 1 - 6 of 6 reviews