about this book
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).
Who should read this book?
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