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Designing Machine Learning Systems : An Iterative Process for...
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Numero oggetto eBay:405847383325
Specifiche dell'oggetto
- Condizione
- ISBN
- 9781098107963
Informazioni su questo prodotto
Product Identifiers
Publisher
O'reilly Media, Incorporated
ISBN-10
1098107969
ISBN-13
9781098107963
eBay Product ID (ePID)
27057246296
Product Key Features
Number of Pages
386 Pages
Language
English
Publication Name
Designing Machine Learning Systems : an Iterative Process for Production-Ready Applications
Subject
Machine Theory, Enterprise Applications / Business Intelligence Tools, Intelligence (Ai) & Semantics
Publication Year
2022
Type
Textbook
Subject Area
Computers
Format
Trade Paperback
Dimensions
Item Height
0.8 in
Item Weight
23.6 Oz
Item Length
9.2 in
Item Width
7.1 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2023-275143
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.31
Synopsis
Many tutorials show you how to develop ML systems from ideation to deployed models. But with constant changes in tooling, those systems can quickly become outdated. Without an intentional design to hold the components together, these systems will become a technical liability, prone to errors and be quick to fall apart. In this book, Chip Huyen provides a framework for designing real-world ML systems that are quick to deploy, reliable, scalable, and iterative. These systems have the capacity to learn from new data, improve on past mistakes, and adapt to changing requirements and environments. Youà Ã?Â[ ll learn everything from project scoping, data management, model development, deployment, and infrastructure to team structure and business analysis. Learn the challenges and requirements of an ML system in production Build training data with different sampling and labeling methods Leverage best techniques to engineer features for your ML models to avoid data leakage Select, develop, debug, and evaluate ML models that are best suit for your tasks Deploy different types of ML systems for different hardware Explore major infrastructural choices and hardware designs Understand the human side of ML, including integrating ML into business, user experience, and team structure, 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
LC Classification Number
Q325.5
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