Large Language Models - The Hard Part
$167.91
- Paperback
350 pages
- Release Date
31 May 2026
Summary
Large language models (LLMs) have transformed natural language processing, but deploying them in applications introduces numerous technical challenges. Large Language Models: The Hard Parts offers a clear, practical examination of the limitations developers and ML engineers face when building LLM-powered applications. With a focus on implementation pitfalls (not just capabilities) this book provides actionable strategies supported by reproducible Python code and open source tools.
Book Details
| ISBN-13: | 9798341622524 |
|---|---|
| Author: | Tharsis T.P. Souza |
| Publisher: | O'Reilly Media |
| Imprint: | O'Reilly Media |
| Format: | Paperback |
| Number of Pages: | 350 |
| Release Date: | 31 May 2026 |
| Dimensions: | 232mm x 178mm |
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About The Author
Tharsis T.P. Souza
Thársis Souza
Dr. Thársis Souza is a computer scientist and product leader specializing in AI-driven products. He is a former Lecturer in Columbia University’s Master of Science program in Applied Analytics and has held senior leadership roles at some of the world’s largest hedge funds. With 20 years of experience delivering technology products across startups and Fortune 500 companies globally, Dr. Souza is the author of numerous scholarly publications and a frequent speaker at academic and industry conferences. Dr. Souza holds a Ph.D. in Computer Science from UCL (University College London), following an M.Phil. and M.Sc. in Computer Science and a B.Sc. in Computer Engineering.
Jonathan Regenstein
Jonathan Regenstein currently serves as the Head of AI/ML in Financial Services at Snowflake, a role he has held since May 2023. Based in Atlanta, Georgia, he operates in a hybrid working environment, showcasing his expertise in artificial intelligence and machine learning within the financial sector. In addition to his professional endeavors, Jonathan is a dedicated academic. He is a Research Affiliate at the Georgia Tech Financial Services Innovation Lab, a position he has maintained since October 2020. Furthermore, since August 2022, he has been imparting his knowledge as an Adjunct Professor at Kennesaw State University’s School of Data Science and Analytics.
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