Книга Modern Approach To Machine Learning and LLMs Caleb Kaiser

Modern Approach To Machine Learning and LLMs

Автор: Caleb Kaiser
Език: Английски език
Корици: С меки корици
Издател: John Wiley & Sons Inc
Наличност: Външен склад в ограничено количество
Изпращаме след 11-15 дни
31.61 61.82 лв
A much-needed guide to implementing new technology in workspaces From experts in the field comes Mac...

Информация за книгата

Автор
Език
Английски език
Корици
Книга - С меки корици
Издадена
2024
страници
240
EAN
9781394249633
Enbook ID
44464303
Издател
Теглоt
358

Пълно описание

A much-needed guide to implementing new technology in workspaces From experts in the field comes Machine Learning Upgrade: A Data Scientist's Guide to MLOps, LLMs, and ML Infrastructure, a book that provides data scientists and managers with best practices at the intersection of management, large language models (LLMs), machine learning, and data science. This groundbreaking book will change the way that you view the pipeline of data science. The authors provide an introduction to modern machine learning, showing you how it can be viewed as a holistic, end-to-end system—not just shiny new gadget in an otherwise unchanged operational structure. By adopting a data-centric view of the world, you can begin to see unstructured data and LLMs as the foundation upon which you can build countless applications and business solutions. This book explores a whole world of decision making that hasn't been codified yet, enabling you to forge the future using emerging best practices. Gain an understanding of the intersection between large language models and unstructured dataFollow the process of building an LLM-powered application while leveraging MLOps techniques such as data versioning and experiment trackingDiscover best practices for training, fine tuning, and evaluating LLMsIntegrate LLM applications within larger systems, monitor their performance, and retrain them on new data This book is indispensable for data professionals and business leaders looking to understand LLMs and the entire data science pipeline.

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