Practical Generative Ai: From Concept to Deployment: Building and Deploying Ethical AI-Powered Solutions
暫譯: 實用生成式人工智慧：從概念到部署：構建與部署道德的人工智慧解決方案
Singh, Pramod, McKeone, James
- 出版商: Apress
- 出版日期: 2026-04-02
- 售價: $3,140
- 貴賓價: 9.5 折 $2,983
- 語言: 英文
- 頁數: 185
- 裝訂: Quality Paper - also called trade paper
- ISBN: 9798868814785
- ISBN-13: 9798868814785
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相關分類:
Large language model、Prompt Engineering
海外代購書籍(需單獨結帳)
商品描述
This book is a comprehensive guide that immerses you into the world of building and deploying AI-powered solutions. You are introduced to the core architecture of LLMs and equipped with essential Best Data Practices (BDPs) for utilizing Generative AI responsibly, ensuring ethical and efficient AI deployment.
The book starts with the foundational aspects of Generative AI application development. You will learn the nuances of data handling in Generative AI apps, from working with embeddings to managing unstructured and structured data, and handling Personally Identifiable Information (PII) data. The exploration extends to understanding the differences between deterministic and LLM-based data synthesis and integrating Generative AI apps with enterprise data, providing you with practical insights into leveraging data effectively for intelligent applications. A chapter on prompt engineering explains the importance of prompts in AI interactions, covering a spectrum of techniques and pitfalls while offering exercises to enhance prompt engineering skills. As you progress through the book, you take a journey from conceptualization to production and deployment of Generative AI applications. You discover the essentials of Generative AI application development, gain insights into the pathway from ideation to production, and explore the intricacies of LLM selection and fine-tuning.
The book equips you with the knowledge and tools necessary to navigate the complex terrain of AI development and deployment, making it an indispensable resource for AI enthusiasts, developers, and business leaders alike.
What You Will Learn
- Know the core architecture of LLMs and how these models have revolutionized AI applications
- Handle various data types, including unstructured, structured, and personally identifiable information (PII) data in Generative AI applications
- Understand prompt engineering and its importance in AI interactions and applications
- Understand modular design of Generative AI apps, essential backend and frontend components, and the unique principles guiding Generative AI app design
Who This Book Is For
From AI enthusiasts exploring the field to software developers seeking practical insights, and business leaders looking to harness the power of AI for organizational growth and innovation
商品描述(中文翻譯)
這本書是一本全面的指南，讓您深入了解構建和部署 AI 驅動解決方案的世界。您將了解大型語言模型（LLMs）的核心架構，並掌握負責任地利用生成式 AI 的基本數據最佳實踐（Best Data Practices, BDPs），確保道德和高效的 AI 部署。
本書從生成式 AI 應用開發的基礎方面開始。您將學習在生成式 AI 應用中處理數據的細微差別，從處理嵌入（embeddings）到管理非結構化和結構化數據，以及處理個人可識別信息（Personally Identifiable Information, PII）數據。探索還擴展到理解確定性數據合成和基於 LLM 的數據合成之間的差異，並將生成式 AI 應用與企業數據集成，為您提供有效利用數據以實現智能應用的實用見解。一章關於提示工程（prompt engineering）的內容解釋了提示在 AI 互動中的重要性，涵蓋了一系列技術和陷阱，同時提供練習以增強提示工程技能。隨著您在書中的進展，您將從概念化到生成式 AI 應用的生產和部署進行一段旅程。您將發現生成式 AI 應用開發的要素，獲得從構思到生產的途徑見解，並探索 LLM 選擇和微調的複雜性。
本書為您提供了在 AI 開發和部署的複雜領域中導航所需的知識和工具，使其成為 AI 愛好者、開發人員和商業領導者不可或缺的資源。
您將學到的內容：
- 知道 LLM 的核心架構以及這些模型如何徹底改變 AI 應用
- 在生成式 AI 應用中處理各種數據類型，包括非結構化、結構化和個人可識別信息（PII）數據
- 理解提示工程及其在 AI 互動和應用中的重要性
- 理解生成式 AI 應用的模組化設計、基本的後端和前端組件，以及指導生成式 AI 應用設計的獨特原則
本書適合對象：
從探索該領域的 AI 愛好者，到尋求實用見解的軟體開發人員，以及希望利用 AI 助力組織增長和創新的商業領導者。
作者簡介
Pramod Singh is an expert Associate Partner at Bain & Company, where he leads the Data Science & Machine Learning guild in the Asia Pacific region as part of Bain's Advanced Analytics Group. With over 15 years of experience in Data Science, Pramod specializes in building large-scale machine learning systems and leading advanced analytics teams. He also heads Bain's Generative AI ringfence group in APAC and is a published author of five books in machine learning and distributed computing.
In his role, Pramod engages with Bain's clients across various industries and geographic locations, including India, Australia, Singapore, Thailand, South Korea, and the wider Asia Pacific region. Over his four year tenure at Bain & Co., he has advised clients on Generative AI solutions, large-scale ML deployments, analytics adoption, tech stack overhauls, data strategy, and responsible AI. His deep industry expertise extends to the retail, telecom, and financial services sectors.
James McKeone is a dedicated data scientist with a passion for solving real-world problems. He excels in crafting innovative solutions and defining architectures for end-to-end data science products. Specializing in Generative AI development, James thrives on leading cutting-edge projects and building effective teams. With extensive experience in writing data science solutions and managing teams, he has successfully delivered projects to stakeholders at all levels. James brings cross-disciplinary expertise from various industries and is committed to achieving measurable results through innovation and collaboration. He has a proven track record of delivering successful data science solutions to stakeholders across various levels of seniority, from technical teams to boards of some of Australia's largest companies. As a leader, he has managed teams of up to five data scientists and data engineers, fostering a culture of safety and active feedback within his teams.
作者簡介(中文翻譯)
Pramod Singh 是貝恩公司（Bain & Company）的一位專家合夥人，負責亞太地區的數據科學與機器學習公會，並且是貝恩的高級分析集團的一部分。擁有超過15年的數據科學經驗，Pramod 專注於構建大規模機器學習系統並領導高級分析團隊。他還負責貝恩在亞太地區的生成式人工智慧（Generative AI）專案小組，並且是五本有關機器學習和分散式計算的書籍的出版作者。
在他的職位中，Pramod 與貝恩的客戶在各行各業和地理位置上進行合作，包括印度、澳大利亞、新加坡、泰國、南韓以及更廣泛的亞太地區。在貝恩公司工作的四年期間，他為客戶提供有關生成式人工智慧解決方案、大規模機器學習部署、分析採用、技術堆疊重構、數據策略和負責任的人工智慧的建議。他在零售、電信和金融服務行業擁有深厚的行業專業知識。
James McKeone 是一位專注的數據科學家，熱衷於解決現實世界的問題。他擅長於設計創新的解決方案並定義端到端數據科學產品的架構。專注於生成式人工智慧開發，James 喜歡領導尖端專案並建立高效的團隊。擁有豐富的數據科學解決方案撰寫和團隊管理經驗，他成功地向各級利益相關者交付專案。James 帶來了來自各行各業的跨學科專業知識，並致力於通過創新和合作實現可衡量的成果。他在向各級高層（從技術團隊到澳大利亞一些大型公司的董事會）交付成功的數據科學解決方案方面擁有良好的記錄。作為一名領導者，他管理過多達五名數據科學家和數據工程師的團隊，並在團隊內培養安全和積極反饋的文化。
