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
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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
Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications
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
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
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Product Details
| BN ID: | 2940190991253 |
|---|---|
| Publisher: | Ascent Audio |
| Publication date: | 11/19/2024 |
| Edition description: | Unabridged |
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