Synthesized answer
The book "Designing Machine Learning Systems" offers a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive [3]. It covers how to engineer data, choose appropriate metrics for business problems, and automate the lifecycle of models, including development, evaluation, deployment, and updating [1, 2]. The design decisions, such as data processing, feature selection, retraining frequency, and monitoring strategies, are considered in relation to how they help the system achieve its overall objectives [3].
The iterative framework presented in the book utilizes actual case studies and references [1, 3]. It aims to help readers tackle scenarios like building a monitoring system for detecting and addressing production issues, and architecting an ML platform that can be used for multiple use cases [1]. The book also touches on developing responsible ML systems [1]. The passages do not provide a direct explanation of the key concepts in the author's own words, but rather outline the scope and approach of the book.
Synthesized from the book passages below. Chat with the book on Feynman for follow-up.
From the book
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
Categories: Computers Pages: 388 Snippet: 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 ...
Description: 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…
Title: Designing Machine Learning Systems by Chip Huyen