Name: Machine Learning Upgrade: A Data Scientist's Guide to Mlops, Llms, and ML Infrastructure: A Data Scientist's Guide to Mlops, Llms, and ML Infrastructu (en Inglés)
SKU: 56147533
Price: 40.10 USD
Availability: InStock
Author: Kehrer, Kristen ; Kaiser, Caleb
ISBN: 9781394249633

portada Machine Learning Upgrade: A Data Scientist's Guide to Mlops, Llms, and ML Infrastructure: A Data Scientist's Guide to Mlops, Llms, and ML Infrastructu (en Inglés)

Machine Learning Upgrade: A Data Scientist's Guide to Mlops, Llms, and ML Infrastructure: A Data Scientist's Guide to Mlops, Llms, and ML Infrastructu (en Inglés)

Formato

Libro Físico

Autor

Kristen Kehrer

Caleb Kaiser

Editorial

Wiley

Idioma

Inglés

N° páginas

256

Encuadernación

Tapa Blanda

ISBN13

9781394249633

N° edición

Categorías

Lenguaje natural y traducción automática
Aprendizaje automático («machine learning»)

Machine Learning Upgrade: A Data Scientist's Guide to Mlops, Llms, and ML Infrastructure: A Data Scientist's Guide to Mlops, Llms, and ML Infrastructu (en Inglés)

Kristen Kehrer (Autor) · Caleb Kaiser (Autor) · Wiley · Tapa Blanda

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Reseña del libro "Machine Learning Upgrade: A Data Scientist's Guide to Mlops, Llms, and ML Infrastructure: A Data Scientist's Guide to Mlops, Llms, and ML Infrastructu (en Inglés)"

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 data Follow the process of building an LLM-powered application while leveraging MLOps techniques such as data versioning and experiment tracking Discover best practices for training, fine tuning, and evaluating LLMs Integrate 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.