Building Agentic AI: Workflows, Fine-Tuning, Optimization, and Deployment
暫譯: 構建自主型人工智慧:工作流程、微調、優化與部署

Ozdemir, Sinan

  • 出版商: Addison Wesley
  • 出版日期: 2025-11-26
  • 售價: $1,940
  • 貴賓價: 9.5$1,843
  • 語言: 英文
  • 頁數: 320
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 0135489687
  • ISBN-13: 9780135489680
  • 相關分類: AI Coding
  • 海外代購書籍(需單獨結帳)

相關主題

商品描述

Transform Your Business with Intelligent AI to Drive Outcomes

Building reactive AI applications and chatbots is no longer enough. The competitive advantage belongs to those who can build AI that can respond, reason, plan, and execute. Building Agentic AI: Workflows, Fine-Tuning, Optimization, and Deployment takes you beyond basic chatbots to create fully functional, autonomous agents that automate real workflows, enhance human decision-making, and drive measurable business outcomes across high-impact domains like customer support, finance, and research.

Whether you're a developer deploying your first model, a data scientist exploring multi-agent systems and distilled LLMs, or a product manager integrating AI workflows and embedding models, this practical handbook provides tried and tested blueprints for building production-ready systems. Harness the power of reasoning models for applications like computer use, multimodal systems to work with all kinds of data, and fine-tuning techniques to get the most out of AI. Learn to test, monitor, and optimize agentic systems to keep them reliable and cost-effective at enterprise scale.

Master the complete agentic AI pipeline

  • Design adaptive AI agents with memory, tool use, and collaborative reasoning capabilities
  • Build robust RAG workflows using embeddings, vector databases, and LangGraph state management
  • Implement comprehensive evaluation frameworks beyond accuracy, including precision, recall, and latency metrics
  • Deploy multimodal AI systems that seamlessly integrate text, vision, audio, and code generation
  • Optimize models for production through fine-tuning, quantization, and speculative decoding techniques
  • Navigate the bleeding edge of reasoning LLMs and computer-use capabilities
  • Balance cost, speed, accuracy, and privacy in real-world deployment scenarios
  • Create hybrid architectures that combine multiple agents for complex enterprise applications

Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

商品描述(中文翻譯)

利用智能 AI 轉型您的業務以驅動成果

建立反應式 AI 應用程式和聊天機器人已經不再足夠。競爭優勢屬於那些能夠構建能夠回應、推理、計劃和執行的 AI 的人。構建代理 AI:工作流程、微調、優化和部署 帶您超越基本的聊天機器人,創建完全功能的自主代理,這些代理自動化真實的工作流程,增強人類的決策能力,並在客戶支持、金融和研究等高影響領域推動可衡量的業務成果。

無論您是部署第一個模型的開發人員、探索多代理系統和精煉 LLM 的數據科學家,還是整合 AI 工作流程和嵌入模型的產品經理,本實用手冊提供了經過驗證的藍圖,用於構建生產就緒的系統。利用推理模型的力量,應用於計算機使用、多模態系統以處理各種數據,以及微調技術以充分發揮 AI 的潛力。學習測試、監控和優化代理系統,以保持其在企業規模上的可靠性和成本效益。

掌握完整的代理 AI 管道


  • 設計具有記憶、工具使用和協作推理能力的自適應 AI 代理

  • 使用嵌入、向量數據庫和 LangGraph 狀態管理構建穩健的 RAG 工作流程

  • 實施超越準確度的全面評估框架,包括精確度、召回率和延遲指標

  • 部署無縫整合文本、視覺、音頻和代碼生成的多模態 AI 系統

  • 通過微調、量化和推測解碼技術優化生產模型

  • 駕馭推理 LLM 和計算機使用能力的前沿技術

  • 在現實世界的部署場景中平衡成本、速度、準確性和隱私

  • 創建結合多個代理的混合架構,以應對複雜的企業應用

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作者簡介

Sinan Ozdemir is an AI expert and entrepreneur with a master's degree in pure mathematics from Johns Hopkins University. He founded Kylie.ai, patented agentic tool use there in 2018, participated in Y Combinator, and exited the company in 2019. Sinan is the author of Quick Start Guide to Large Language Models, Second Edition (Addison-Wesley, 2025), and cohosts the Practically Intelligent podcast. He has created several popular AI courses for Pearson on O'Reilly.

作者簡介(中文翻譯)

Sinan Ozdemir 是一位人工智慧專家和企業家,擁有約翰霍普金斯大學的純數學碩士學位。他於2018年創立了Kylie.ai,並在那裡獲得了代理工具使用的專利,參加了Y Combinator,並於2019年退出了該公司。Sinan是《大型語言模型快速入門指南,第二版》(Addison-Wesley,2025)的作者,並共同主持了Practically Intelligent 播客。他為Pearson在O'Reilly上創建了幾個受歡迎的人工智慧課程。