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Available from Humble Book Bundle

by Chip Huyen

To Read

An iterative process for production-ready applications

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