Machine Learning Upgrade

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A01=Caleb Kaiser
A01=Kristen Kehrer
Age Group_Uncategorized
Age Group_Uncategorized
Author_Caleb Kaiser
Author_Kristen Kehrer
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BI
business intelligence
Category1=Non-Fiction
Category=UNC
Category=UNF
Category=UYQL
Category=UYQM
COP=United States
data science book
Delivery_Delivery within 10-20 working days
eq_bestseller
eq_computing
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Language_English
large language models
llm applications
llm book
llm coding
llm development
llm engineering
LLMs
Machine learning
machine learning development
ML
PA=Available
Price_€20 to €50
prompt engineering
PS=Active
softlaunch
training ai

Product details

  • ISBN 9781394249633
  • Weight: 272g
  • Dimensions: 150 x 226mm
  • Publication Date: 08 Aug 2024
  • Publisher: John Wiley & Sons Inc
  • Publication City/Country: US
  • Product Form: Paperback
  • Language: English
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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.

Kristen Kehrer has been providing innovative and practical statistical modeling solutions since 2010. In 2018, she achieved recognition as a LinkedIn Top Voice in Data Science & Analytics. Kristen is also the founder of Data Moves Me, LLC.

Caleb Kaiser is a Full Stack Engineer at Comet. Caleb was previously on the Founding Team at Cortex Labs. Caleb also worked at Scribe Media on the Author Platform Team.

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