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by Kristen Kehrer (Author), Caleb Kaiser (Author)
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.
Back Jacket
An end-to-end framework for developing Large Language Model (LLM)-based applications
Traditionally, there has been a divide between data scientists and software engineers. With the advent of LLMs, however, this has changed. Machine learning is no longer primarily a tool for data analysis, but is now a fundamental feature of modern software applications. In Machine Learning Upgrade, data scientists are given a comprehensive framework not just for understanding LLMs, but for building efficient, reproducible, and scalable LLM applications.
Written by leading data scientists, this book brings you up to date on the current state of LLM technology and offers both a conceptual and hands-on overview of how it can be most responsibly integrated into business. Readers will follow along as the authors build an LLM-powered application, providing a concrete example of their framework in action. Best practices for data versioning, experiment tracking, model monitoring, and ethical considerations are also central.
Data professionals of all levels looking for a holistic understanding of LLM aplications using the latest technologies and practices will benefit from this book. By adopting a data-centric view, we can identify opportunities to integrate LLMs and drive business success.
