Building Reliable AI Systems: Applications and Agents You Can Trust (Paperback)
暫譯: 打造值得信賴的可靠 AI 系統：值得信賴的應用程式與代理程式

Shahani, Rush

Building Reliable AI Systems: Applications and Agents You Can Trust (Paperback)

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商品描述

Get the eBook free when you register your print book at Manning.

This book shows you exactly how to guide large language models from research prototypes to scalable, robust, and efficient production systems. From model training to maintenance, an engineer will find everything they need to work with LLMs in this one-stop guide.

This book complements Sebastian Raschka's Build a Large Language Model (From Scratch), which takes a hands-on, ground-up approach to constructing LLMs. While Raschka's book focuses on building models from scratch, this book centers on deploying, optimizing, and maintaining reliable, production-grade AI systems.

Inside Building Reliable AI Systems you'll learn how to:

- Deploy LLMs into production
- Detect and reduce hallucinations
- Mitigate bias
- Optimize LLM performance and resource usage
- Advanced prompt engineering techniques
- Build intelligent agents and Retrieval-Augmented Generation

Building Reliable AI Systems is a guide to putting LLMs into production in the real world. The book bridges the gap between theory and practice. You'll go beyond basics like prompting into advanced optimizations: intelligent agents, Retrieval Augmented Generation (RAG), and in-depth solutions for mitigating hallucinations and bias.

About the book

Building Reliable AI Systems is a comprehensive guide to creating LLM-based apps that are faster and more accurate. It takes you from training to production and beyond into the ongoing maintenance of an LLM. In each chapter, you'll find in-depth code samples and hands-on projects--including building a RAG-powered chatbot and an agent created with LangChain. Deploying an LLM can be costly, so you'll love the performance optimization techniques--prompt optimization, model compression, and quantization--that make your LLMs quicker and more efficient. Throughout, real-world case studies from e-commerce, healthcare, and legal work give concrete examples of how businesses have solved some of LLMs common problems.

About the reader

For data scientists or software engineers confident in Python and NLP.

About the author

Rush Shahani is a seasoned AI Engineer and CTO of Persana AI, a YCombinator-backed startup. At Persana, he leads the development of natural language processing and large language model systems that provide actionable insights to companies in order to drive revenue growth. His experience includes building AI systems at companies like LinkedIn, Element AI, and Shopify.

商品描述(中文翻譯)

在 Manning 註冊您的紙本書，即可免費取得電子書。

本書將精確示範如何引導大型語言模型（LLM）從研究原型發展為可擴展、穩健且高效的生產系統。從模型訓練到維護，工程師與 LLM 合作所需的一切內容，都能在這本一站式指南中找到。

本書是 Sebastian Raschka 所著《Build a Large Language Model (From Scratch)》的互補讀物。後者採取實作導向、由底層開始的方法，帶領讀者建構 LLM。Raschka 的著作著重於從零開始建立模型，而本書則聚焦於部署、最佳化及維護可靠、符合生產環境要求的 AI 系統。

在《Building Reliable AI Systems》中，您將學會如何：

- 將 LLM 部署至生產環境
- 偵測並降低幻覺（hallucination）
- 降低偏見
- 最佳化 LLM 的效能與資源使用量
- 採用進階提示工程（prompt engineering）技術
- 建立智慧型代理程式（intelligent agents）與檢索增強生成（Retrieval-Augmented Generation）

《Building Reliable AI Systems》是一本將 LLM 實際導入真實世界生產環境的指南。本書彌合理論與實務之間的落差，帶您超越提示（prompting）等基礎概念，深入學習進階最佳化技術，包括智慧型代理程式、檢索增強生成（Retrieval-Augmented Generation，RAG），以及降低幻覺與偏見的深度解決方案。

關於本書

《Building Reliable AI Systems》是一本完整指南，教您建立速度更快、準確度更高的 LLM 應用程式。本書將帶您從訓練一路進入生產環境，並進一步涵蓋 LLM 的持續維護。每一章都提供深入的程式碼範例與實作專案，包括建立由 RAG 驅動的聊天機器人，以及使用 LangChain 建立代理程式。

部署 LLM 可能代價高昂，因此您一定會喜歡本書介紹的效能最佳化技術，包括提示最佳化、模型壓縮與量化；這些技術能讓 LLM 執行速度更快、效率更高。全書穿插來自電子商務、醫療保健與法律領域的真實世界案例研究，具體說明企業如何解決 LLM 常見的一些問題。

適合讀者

適合熟悉 Python 與 NLP，且具備信心的資料科學家或軟體工程師。

關於作者

Rush Shahani 是資深 AI 工程師，也是獲 Y Combinator 支持的新創公司 Persana AI 的 CTO。在 Persana，他負責帶領自然語言處理與大型語言模型系統的開發，為企業提供可付諸行動的洞察，以推動營收成長。他曾在 LinkedIn、Element AI 與 Shopify 等公司建置 AI 系統。

作者簡介

Rush Shahani is a seasoned AI Engineer and CTO of Persana AI, a YCombinator-backed startup. At Persana, he leads the development of natural language processing and large language model systems that provide actionable insights to companies in order to drive revenue growth. His experience includes building AI systems at companies like LinkedIn, Element AI, and Shopify.

作者簡介(中文翻譯)

Rush Shahani 是一位資深的 AI Engineer，也是 Persana AI 的 CTO；Persana AI 是一家獲得 Y Combinator 支持的新創公司。在 Persana，他負責帶領自然語言處理（natural language processing）與大型語言模型（large language model）系統的開發，為企業提供可付諸行動的洞察，進而推動營收成長。他曾在 LinkedIn、Element AI 和 Shopify 等公司建置 AI 系統，累積了豐富經驗。

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