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Machine Learning Upgrade

- A Data Scientist's Guide to MLOps, LLMs, and ML Infrastructure
Af: Caleb Kaiser, Kristen Kehrer Engelsk Paperback

Machine Learning Upgrade

- A Data Scientist's Guide to MLOps, LLMs, and ML Infrastructure
Af: Caleb Kaiser, Kristen Kehrer Engelsk Paperback
Tjek vores konkurrenters priser
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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Tjek vores konkurrenters priser
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.
Produktdetaljer
Sprog: Engelsk
Sider: 240
ISBN-13: 9781394249633
Indbinding: Paperback
Udgave:
ISBN-10: 1394249632
Udg. Dato: 8 aug 2024
Længde: 16mm
Bredde: 229mm
Højde: 152mm
Forlag: John Wiley & Sons Inc
Oplagsdato: 8 aug 2024
Forfatter(e): Caleb Kaiser, Kristen Kehrer
Forfatter(e) Caleb Kaiser, Kristen Kehrer


Kategori Naturligt sprog og maskinoversættelse


ISBN-13 9781394249633


Sprog Engelsk


Indbinding Paperback


Sider 240


Udgave


Længde 16mm


Bredde 229mm


Højde 152mm


Udg. Dato 8 aug 2024


Oplagsdato 8 aug 2024


Forlag John Wiley & Sons Inc