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Designing Machine Learning Systems : An Iterative Process for Production-Ready Applications
Huyen, Chip ¤Ó O'Reilly Media
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9781098107963/1098107969
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  • "This is, simply, the very best book you can read about how to build, deploy, and scale machine learning models at a company for maximum impact. Chip is a masterful teacher, and the breadth and depth of her knowledge is unparalleled." - Josh Wills, Software Engineer at WeaveGrid and former Director of Data Engineering, Slack "There is so much information one needs to know to be an effective machine learning engineer. It's hard to cut through the chaff to get the most relevant information, but Chip has done that admirably with this book. If you are serious about ML in production, and care about how to design and implement ML systems end to end, this book is essential." - Laurence Moroney, AI and ML Lead, Google "One of the best resources that focuses on the first principles behind designing ML systems for production. A must-read to navigate the ephemeral landscape of tooling and platform options." - Goku Mohandas, Founder of Made With ML "Chip's manual is the book we deserve and the one we need right now. In a blooming but chaotic ecosystem, this principled view on end-to-end ML is both your map and your compass: a must-read for practitioners inside and outside of Big Tech¡ªespecially those working at 'reasonable scale.' This book will also appeal to data leaders looking for best practices on how to deploy, manage, and monitor systems in the wild." - Jacopo Tagliabue, Director of AI, Coveo; Adj. Professor of MLSys, NYU "Chip is truly a world-class expert on machine learning systems, as well as a brilliant writer. Both are evident in this book, which is a fantastic resource for anyone looking to learn about this topic." - Andrey Kurenkov, PhD Candidate at the Stanford AI Lab
  • Chapter Page Preface ix 1. Overview of Machine Learning Systems 1 When to Use Machine Learning 3 Machine Learning Use Cases 9 Understanding Machine Learning Systems 12 Machine Learning in Research Versus in Production 12 Machine Learning Systems Versus Traditional Software 22 Summary 23 2. Introduction to Machine Learning Systems Design 25 Business and ML Objectives 26 Requirements for ML Systems 29 Reliability 29 Scalability 30 Maintainability 31 Adaptability 31 Iterative Process 32 Framing ML Problems 35 Types of ML Tasks 36 Objective Functions 40 Mind Versus Data 43 Summary 46 3. Data Engineering Fundamentals 49 Data Sources 50 Data Formats 53 JSON 54 Row-Major Versus Column-Major Format 54 Text Versus Binary Format 57 Data Models 58 Relational Model 59 NoSQL 63 Structured Versus Unstructured Data 66 Data Storage Engines and Processing 67 Transactional and Analytical Processing 67 ...
  • Huyen, Chip [Àú]
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