Balance performance with cost optimization to unlock the potential of AI

With the rise of AI and machine learning, large language models (LLMs) have become increasingly popular, but their high computational costs can be a barrier to entry for many organizations. This book offers cost-effective approaches to building and deploying LLMs. At each stage of the process, from model selection and prompt engineering to fine tuning and deployment, you can minimize costs without unduly sacrificing performance.

Written for developers and data scientists, Large Language Model-Based Solutions provides the practical, technical knowledge needed to implement valuable generative AI applications like search systems, agent assists, and autonomous agents. The book explores techniques for optimizing inference, such as model quantization and pruning, as well as opportunities for reducing costs at the infrastructure level. It also considers future trends in LLM cost optimization, so you can remain competitive for the next stage in generative AI.

Written by one of Amazon's leading data scientists, this book empowers you to overcome the challenges associated with LLMs and successfully implement generative AI.

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.

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. 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.