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  • 1
    Online Resource
    Online Resource
    Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo :O'Reilly,
    UID:
    almafu_BV048277459
    Format: 1 Online-Ressource (xvi, 367 Seiten) : , Illustrationen, Diagramme.
    Edition: First edition
    ISBN: 978-1-098-10793-2
    Content: 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 But with constant changes in tooling, those systems can quickly become outdated.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. Youâ??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...
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-1-098-10796-3
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Maschinelles Lernen ; Systementwurf
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Online Resource
    Online Resource
    O'Reilly Media
    UID:
    kobvindex_ZLB34974908
    ISBN: 9781098107932 , 9781098107918
    Content: " 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 decision8212 such as how to process and create training data, which features to use, how often to retrain models, and what to monitor8212 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 "
    Content: Biographisches: " Chip Huyen (〈a href=https://huyenchip.com〉https://huyenchip.com〈/a〉) 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 (〈a href=https://discord.com/invite/Mw77HPrgjF〉https://discord.com/invite/Mw77HPrgjF〈/a〉). "
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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