Synthesized answer
The provided passages do not explicitly state what questions remain unanswered by the book "Designing Machine Learning Systems" [2].
However, the passages do indicate that the book aims to help readers tackle scenarios such as engineering data and choosing the right metrics to solve a business problem, automating the continuous development, evaluation, deployment, and updating of models, developing a monitoring system for production issues, architecting an ML platform, and developing responsible ML systems [1, 3]. The book also teaches a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive [4].
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
Title: Designing Machine Learning Systems by Chip Huyen
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…