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

Über die Autorin bzw. den Autor

Chip Huyen (https: //huyenchip.com) is a co-founder of Claypot AI, a platform for real-time machine learning. Through her work at NVIDIA, Netflix, and Snorkel AI, she has helped some of the world's largest organizations develop and deploy machine learning systems. She teaches CS 329S: Machine Learning Systems Design at Stanford, whose lecture notes this book is based on.

LinkedIn included her among Top Voices in Software Development (2019) and Top Voices in Data Science & AI (2020). She is also the author of four bestselling Vietnamese books, including the series Xach ba lo len va Di (Pack Your Bag and Go). She also runs a Discord server on MLOps with over 6,000 members (https: //discord.com/invite/Mw77HPrgjF).

Von der hinteren Coverseite

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