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Название: Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications
Автор: 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. 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.
Автор: 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. 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.