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 (Gen AI) 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 Gen AI, 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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SHREYAS SUBRAMANIAN, Ph D, is a principal data scientist at AWS, one of the largest organizations building and providing large language models for enterprise use. He is currently advising both internal Amazon teams and large enterprise customers on building, tuning, and deploying Generative AI applications at scale. Shreyas runs machine learning-focused cost optimization workshops, helping them reduce the costs of machine learning applications on the cloud. Shreyas also actively participates in cutting-edge research and development of advanced training, tuning and deployment techniques for foundation models.
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