Large Language Model-Based Solutions
How to Deliver Value with Cost-Effective Generative AI Applications
カートのアイテムが多すぎます
カートに追加できませんでした。
ウィッシュリストに追加できませんでした。
ほしい物リストの削除に失敗しました。
ポッドキャストのフォローに失敗しました
ポッドキャストのフォロー解除に失敗しました
Large Language Model-Based Solutions
￥3,010 で購入
-
ナレーター：
-
Daniel Henning
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
©2024 John Wiley & Sons, Inc. (P)2024 Ascent Audio