Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

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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, co-founder 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
Developing responsible ML systems

I’m Chip Huyen, a writer and computer scientist. I grew up chasing grasshoppers in a small rice-farming village in Vietnam.

I work in the intersection of AI, data, and storytelling. Previously, I built machine learning tools at NVIDIA, Snorkel AI, Netflix, and founded an AI infrastructure startup (acquired).

I also taught Machine Learning Systems Design at Stanford.

My last book, Designing Machine Learning Systems, is an Amazon bestseller in AI and has been translated into over 10 languages (very proud!).

In my free time, I like writing stories. I'm also the author of 4 Vietnamese story books.