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Designing Large Language Model Applications: A Holistic Approach to Llms

by Suhas Pai
Save 34% Save 34%
Current price ₹5,488.00
Original price ₹8,319.00
Original price ₹8,319.00
Original price ₹8,319.00
(-34%)
₹5,488.00
Current price ₹5,488.00

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Book cover type: Paperback
  • ISBN13: 9781098150501
  • Binding: Paperback
  • Subject: N/A
  • Publisher: O'Reilly Media
  • Publisher Imprint: O'Reilly Media
  • Publication Date:
  • Pages: 364
  • Original Price: GBP 63.99
  • Language: English
  • Edition: N/A
  • Item Weight: 581 grams
  • BISAC Subject(s): Data Science / Machine Learning, Artificial Intelligence / Natural Language Processing, and Machine Theory

Transformer-based language models are powerful tools for solving a variety of language tasks and represent a phase shift in the field of natural language processing. But the transition from demos and prototypes to full-fledged applications has been slow. With this book, you'll learn the tools, techniques, and playbooks for building useful products that incorporate the power of language models.

Experienced ML researcher Suhas Pai provides practical advice on dealing with commonly observed failure modes and counteracting the current limitations of state-of-the-art models. You'll take a comprehensive deep dive into the Transformer architecture and its variants. And you'll get up-to-date with the taxonomy of language models, which can offer insight into which models are better at which tasks.

You'll learn:

  • Clever ways to deal with failure modes of current state-of-the-art language models, and methods to exploit their strengths for building useful products
  • How to develop an intuition about the Transformer architecture and the impact of each architectural decision
  • Ways to adapt pretrained language models to your own domain and use cases
  • How to select a language model for your domain and task from among the choices available, and how to deal with the build-versus-buy conundrum
  • Effective fine-tuning and parameter efficient fine-tuning, and few-shot and zero-shot learning techniques
  • How to interface language models with external tools and integrate them into an existing software ecosystem

Pai, Suhas: - Suhas Pai is an experienced machine learning researcher, having worked in the tech industry for over a decade. He is the co-founder, CTO, and ML Research Lead at Hudson Labs, a Y-Combinator backed AI & Fintech startup, since 2020. At Hudson Labs, Suhas invented several novel techniques in the area of domain-adapted LLMs, text ranking, and representation learning, that fully power the core features of Hudson Lab's products. He has contributed to the development of several open-source LLMs, including being the co-lead of the Privacy working group at BigScience, as part of the BLOOM LLM project.

Suhas is active in the ML community, being Chair of the TMLS (Toronto Machine Learning Summit) conference since 2021. He is also a frequent speaker at AI conferences worldwide, and hosts regular seminars discussing the latest research in the field of NLP.

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