Название: Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI ApplicationsАвтор: Shreyas Subramanian
Издательство: Wiley
Год: 2024
Страниц: 221
Язык: английский
Формат: pdf (true), epub (true)
Размер: 10.6 MB, 15.5 MB
Learn to build cost-effective apps using Large Language Models.
In Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications, Principal Data Scientist at Amazon Web Services, Shreyas Subramanian, delivers a practical guide for developers and data scientists who wish to build and deploy cost-effective large language model (LLM)-based solutions. In the book, you'll find coverage of a wide range of key topics, including how to select a model, pre- and post-processing of data, prompt engineering, and instruction fine tuning. Large language models (LLMs) have become a cornerstone of Artificial Intelligence (AI) research and applications, transforming the way we interact with technology and enabling breakthroughs in natural language processing (NLP).
The author sheds light on techniques for optimizing inference, like model quantization and pruning, as well as different and affordable architectures for typical generative AI (GenAI) applications, including search systems, agent assists, and autonomous agents.
While both GenAI and LLMs deal with generating content, their scopes and applications differ. GenAI is a broader term that encompasses AI systems capable of creating various types of content, such as text, images, videos, and other media. LLMs, on the other hand, are a specific class of deep learning models designed to process and understand natural language data. LLMs are used as a core component in GenAI applications to generate human-like text. GenAI applications, on the other hand, use LLMs to create more comprehensive and interactive experiences for users. While LLMs are responsible for understanding and generating human‐like text, GenAI applications utilize these capabilities to create more comprehensive and interactive experiences for users.
You'll also find:
• Effective strategies to address the challenge of the high computational cost associated with LLMs
• Assistance with the complexities of building and deploying affordable generative AI apps, including tuning and inference techniques
• Selection criteria for choosing a model, with particular consideration given to compact, nimble, and domain-specific models
Perfect for developers and data scientists interested in deploying foundational models, or business leaders planning to scale out their use of GenAI, Large Language Model-Based Solutions will also benefit project leaders and managers, technical support staff, and administrators with an interest or stake in the subject.
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