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Cover image for Machine Learning Upgrade  : A Data Scientist's Guide to MLOps, LLMs, and ML Infrastructure.
Title:
Machine Learning Upgrade : A Data Scientist's Guide to MLOps, LLMs, and ML Infrastructure.
Author:
Kehrer, Kristen.
ISBN:
9781394249664

9781394319763

9781394249640
Publication Information:
Newark : John Wiley & Sons, Incorporated, 2024.
Physical Description:
1 online resource (237 p.)
General Note:
Description based upon print version of record.
Contents:
Cover -- Title Page -- Copyright Page -- Contents -- Introduction -- Acknowledgments -- About the Authors -- About the Technical Editor -- Chapter 1 A Gentle Introduction to Modern Machine Learning -- Data Science Is Diverging from Business Intelligence -- From CRISP-DM to Modern, Multicomponent ML Systems -- The Emergence of LLMs Has Increased ML's Power and Complexity -- What You Can Expect from This Book -- Chapter 2 An End-to-End Approach -- Components of a YouTube Search Agent -- Principles of a Production Machine Learning System -- Observability -- Reproducibility -- Interoperability -- Scalability -- Improvability -- A Note on Tools -- Chapter 3 A Data-Centric View -- The Emergence of Foundation Models -- The Role of Off-the-Shelf Components -- The Data-Driven Approach -- A Note on Data Ethics -- Building the Dataset -- Working with Vector Databases -- Data Versioning and Management
Abstract:
This book, authored by Kristen Kehrer and Caleb Kaiser, is a comprehensive guide for data scientists and practitioners in the field of machine learning, focusing on the modern challenges posed by MLOps, large language models (LLMs), and data-centric methodologies. It covers the evolution of machine learning practices from traditional frameworks to advanced systems, emphasizing the importance of data versioning, experiment tracking, and model monitoring. The book is designed to equip readers with practical examples and code to implement best practices in machine learning projects. Intended for professionals and enthusiasts in data science and machine learning, it provides insights into creating and deploying machine learning applications, emphasizing ethical guidelines and modern tools.
Local Note:
John Wiley and Sons
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