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Designing Machine Learning Systems : An Iterative Process for...

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    Subject Area
    Information Science, Software Development
    Publication Name
    Designing Machine Learning Systems
    Publisher
    O'Reilly Media;Inc
    Item Length
    9.2 in
    Subject
    Computer Science
    Publication Year
    2022
    Type
    Textbook
    Format
    Paperback
    Language
    English
    Item Height
    0.8 in
    Author
    Chip Huyen
    Educational Level
    Adult & Further Education
    Personalized
    No
    Level
    Advanced, Proficiency
    Country/Region of Manufacture
    United States
    Item Width
    7.1 in
    Item Weight
    23.6 oz
    Number of Pages
    386 pages
    ISBN
    9781098107963

    關於產品

    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
    Publication Name
    Designing Machine Learning Systems : an Iterative Process for Production-Ready Applications
    Language
    English
    Publication Year
    2022
    Subject
    Machine Theory, Enterprise Applications / Business Intelligence Tools, Intelligence (Ai) & Semantics
    Type
    Textbook
    Author
    Chip Huyen
    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 But with constant changes in tooling, those systems can quickly become outdated.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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        Thank you for neatly packing the books. They have arrived a day or two after being shipped and are in the exact condition as described. Thanks so much!!!
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