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Designing Machine Learning Systems
An Iterative Process for Production-Ready Applications
Author: Chip Huyen
Narrator: Kathleen Li
Unabridged: 12 hr 55 min
Format: Digital Audiobook Download
Publisher: Ascent Audio
Published: 07/08/2025
Categories: Nonfiction, Computers & Technology, Data Science, Software, Artificial Intelligence
Includes:
Bonus Material
Synopsis
Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.
Author Chip Huyen, cofounder of Claypot AI, considers each design decision—such as how to process and create training data, which features to use, how often to retrain models, and what to monitor—in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references.
This book will help you tackle scenarios such as engineering data and choosing the right metrics to solve a business problem; automating the process for continually developing, evaluating, deploying, and updating models; developing a monitoring system to quickly detect and address issues your models might encounter in production; architecting an ML platform that serves across use cases; and developing responsible ML systems.
Author Chip Huyen, cofounder of Claypot AI, considers each design decision—such as how to process and create training data, which features to use, how often to retrain models, and what to monitor—in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references.
This book will help you tackle scenarios such as engineering data and choosing the right metrics to solve a business problem; automating the process for continually developing, evaluating, deploying, and updating models; developing a monitoring system to quickly detect and address issues your models might encounter in production; architecting an ML platform that serves across use cases; and developing responsible ML systems.
