Building Reliable AI Systems: Applications and Agents You Can Trust
暫譯: 建立可靠的 AI 系統：可信賴的應用與代理
Shahani, Rush
- 出版商: Manning
- 出版日期: 2026-09-15
- 售價: $2,380
- 貴賓價: 9.5 折 $2,261
- 語言: 英文
- 頁數: 368
- 裝訂: Quality Paper - also called trade paper
- ISBN: 163343673X
- ISBN-13: 9781633436732
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相關分類:
Large language model
海外代購書籍(需單獨結帳)
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.
- 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註冊您的印刷書籍時可免費獲得電子書。
本書詳細說明了如何將大型語言模型（LLMs）從研究原型引導至可擴展、穩健且高效的生產系統。從模型訓練到維護，工程師在這本一站式指南中將找到與LLMs合作所需的一切。 本書補充了Sebastian Raschka的從零開始構建大型語言模型，該書採取了實踐性、從基礎開始的方式來構建LLMs。雖然Raschka的書專注於從零開始構建模型，但本書則集中於部署、優化和維護可靠的生產級AI系統。 在構建可靠的AI系統中，您將學習如何： - 將LLMs部署到生產環境中- 檢測並減少幻覺
- 減輕偏見
- 優化LLM性能和資源使用
- 進階提示工程技術
- 構建智能代理和檢索增強生成 構建可靠的AI系統是將LLMs投入實際生產的指南。本書彌合了理論與實踐之間的鴻溝。您將超越基本的提示，進入進階優化：智能代理、檢索增強生成（RAG）以及減輕幻覺和偏見的深入解決方案。 關於本書 構建可靠的AI系統是創建基於LLM的應用程序的全面指南，這些應用程序更快且更準確。它將您從訓練帶入生產，並進一步進入LLM的持續維護。在每一章中，您將找到深入的代碼範例和實作專案，包括構建一個RAG驅動的聊天機器人和使用LangChain創建的代理。部署LLM可能成本高昂，因此您會喜歡性能優化技術——提示優化、模型壓縮和量化——使您的LLMs更快且更高效。在整本書中，來自電子商務、醫療保健和法律工作的真實案例研究提供了具體示例，展示企業如何解決一些LLMs常見的問題。 關於讀者 適合對Python和自然語言處理（NLP）有信心的數據科學家或軟體工程師。 關於作者 Rush Shahani是一位資深的AI工程師及Persana AI的首席技術官，該公司是一家獲得YCombinator支持的初創企業。在Persana，他負責開發自然語言處理和大型語言模型系統，為公司提供可行的見解以推動收入增長。他的經驗包括在LinkedIn、Element AI和Shopify等公司構建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 是一位資深的人工智慧工程師及 Persana AI 的首席技術官，該公司是一家獲得 YCombinator 支持的初創企業。在 Persana，他負責開發自然語言處理和大型語言模型系統，這些系統為企業提供可行的見解，以促進收入增長。他的經驗包括在 LinkedIn、Element AI 和 Shopify 等公司構建人工智慧系統。
