Architecting Enterprise AI Applications: A Guide to Designing Reliable, Scalable, and Secure Enterprise-Grade AI Solutions
Cagle, Anton, Ahmed, Ahmed Mohamed Ceifelnasr
相關主題
商品描述
This book explores how to define, design, and maintain enterprise AI applications, exploring the impacts they will have on the teams who work with them.
The book is structured into four parts. In Part 1: Defining Your AI Application, you are introduced to the dynamic interplay between human adaptability and AI specialization, the concept of meta systems, and the mechanics of prediction machines. In Part 2: Designing Your AI Application, the book delves into the anatomy of an AI application, unraveling the intricate relationships among data, machine learning, and reasoners. This section introduces the building blocks and enterprise architectural framework for designing multi-agent systems. Part 3: Maintaining Your AI Application takes a closer look at the ongoing life cycle of AI systems. You are guided through the crucial aspects of testing and test automation, providing a solid foundation for effective development practices. This section covers the critical tasks of security and information curation that ensure the long-term success of enterprise AI applications. The concluding section, Part 4: AI Enabled Teams, navigates the evolving landscape of collaborative efforts between humans and AI. It explores the impact of AI on remote work dynamics and introduces the new roles of the expert persona and the AI handler. This section concludes with a deep dive into the legal and ethical dimensions that AI-enabled teams must navigate.
This book is a comprehensive guide that not only equips developers, architects, and product owners with the technical know-how of AI application development, but also delves into the broader implications for teams and society.
What You Will Learn
- Understand the algorithms and processes that enable AI to make accurate predictions and enhance decision making
- Grasp the concept of metasystems and their role in the design phase of AI applications
- Know how data, machine learning, and reasoners drive the functionality and decision-making capabilities of AI applications
- Know the architectural components necessary for scalable and maintainable multi-agent AI applications
- Understand methodologies for testing AI applications, ensuring their robustness, accuracy, and reliability in real-world applications
- Understand the evolving dynamics of human-AI coordination facing teams in the new enterprise working environment
Who This book Is For
A diverse audience, primarily targeting enterprise architects, middle managers, tech leads, and team leads entrenched in the IT sector or possessing a tech-savvy background, including professionals such as digital marketers. Additionally, tech-savvy individual contributors--ranging from digital content creators and data analysts to administrators and programmers--stand to benefit significantly.
商品描述(中文翻譯)
本書探討如何定義、設計和維護企業 AI 應用程式，並探討這些應用程式對與之合作的團隊所產生的影響。
本書分為四個部分。第一部分：定義您的 AI 應用程式，介紹了人類適應性與 AI 專業化之間的動態互動、元系統的概念以及預測機器的運作原理。第二部分：設計您的 AI 應用程式，深入探討 AI 應用程式的結構，揭示數據、機器學習和推理器之間的複雜關係。本部分介紹了設計多代理系統的基本構件和企業架構框架。第三部分：維護您的 AI 應用程式，仔細研究 AI 系統的持續生命週期。您將了解測試和測試自動化的關鍵方面，為有效的開發實踐提供堅實的基礎。本部分涵蓋了確保企業 AI 應用程式長期成功的安全性和信息策展等關鍵任務。最後一部分：AI 驅動的團隊，探索人類與 AI 之間協作努力的演變格局。它探討了 AI 對遠程工作動態的影響，並介紹了專家角色和 AI 操作員的新角色。本部分最後深入探討了 AI 驅動團隊必須面對的法律和倫理層面。
本書是一部全面的指南，不僅為開發人員、架構師和產品負責人提供 AI 應用程式開發的技術知識，還深入探討了對團隊和社會的更廣泛影響。
您將學到的內容：
- 理解使 AI 能夠做出準確預測和增強決策的算法和過程
- 掌握元系統的概念及其在 AI 應用程式設計階段的角色
- 知道數據、機器學習和推理器如何驅動 AI 應用程式的功能和決策能力
- 知道可擴展和可維護的多代理 AI 應用程式所需的架構組件
- 理解測試 AI 應用程式的方法論，確保其在現實應用中的穩健性、準確性和可靠性
- 理解在新企業工作環境中，團隊面對的人類與 AI 協調的演變動態
本書的讀者對象：
本書面向多元的讀者群，主要針對企業架構師、中層管理者、技術負責人和團隊負責人，特別是那些深耕於 IT 行業或具備技術背景的專業人士，包括數位行銷人員。此外，具備技術背景的個人貢獻者——從數位內容創作者和數據分析師到管理員和程式設計師——也將從中獲益良多。
作者簡介
Anton Cagle is a seasoned leader specializing in cloud automation and AI Ops, boasting over two decades of expertise in enterprise architecture and application design. With a passion for delivering democratized, data-driven solutions and automation, Anton focuses on empowering medium to large-sized companies. His dedication extends to mentoring and coaching engineers at all skill levels, fostering a culture of continuous learning and innovation.
Recognizing the pivotal role of cloud, data, and AI in shaping the future of business software, Anton is on a mission to guide companies beyond basic automation solutions. His goal is to seamlessly integrate big data and machine learning into organizational frameworks, preparing businesses for the next wave of scalable operations. Anton's approach has led to remarkable transformations for clients, including the reduction of deployment process waste, accelerated feature time to market, and the implementation of cutting-edge cloud data architectures.
Ahmed Ceifelnasr Ahmed is a highly skilled ML engineer, data scientist, and cloud engineer with over six years of experience in developing and deploying data-driven solutions. Ahmed specializes in building and fine-tuning machine learning models, leveraging advanced deep learning techniques, and optimizing cloud-based solutions. His expertise extends to cloud engineering and DevOps practices, where he excels in designing and implementing scalable, efficient cloud architectures and automating deployment processes.
With hands-on experience in AWS Cloud environments and a strong background in cloud tools, Ahmed is adept at integrating AI with cloud technologies to create robust, production-ready solutions. He has a proven track record of driving impactful results across various industries, from retail and real estate to fitness and enterprise applications.
Ahmed is committed to continuous learning and growth, always seeking to make a significant impact in the fields of AI, data science, and cloud engineering. His career reflects his dedication to advancing technology, optimizing cloud infrastructure, and fostering innovation through data-driven strategies and cutting-edge technology.
作者簡介(中文翻譯)
安東·卡格爾（Anton Cagle）是一位資深領導者，專注於雲端自動化和人工智慧運營，擁有超過二十年的企業架構和應用設計專業經驗。安東熱衷於提供民主化、數據驅動的解決方案和自動化，並專注於賦能中型到大型企業。他的奉獻精神延伸至指導和培訓各技能水平的工程師，培養持續學習和創新的文化。
安東認識到雲端、數據和人工智慧在塑造商業軟體未來中的關鍵角色，致力於引導企業超越基本的自動化解決方案。他的目標是將大數據和機器學習無縫整合進組織框架，為企業準備迎接下一波可擴展的運營。安東的方法為客戶帶來了顯著的轉型，包括減少部署過程中的浪費、加速功能上市時間，以及實施尖端的雲端數據架構。
艾哈邁德·塞費爾納斯·艾哈邁德（Ahmed Ceifelnasr Ahmed）是一位高技能的機器學習工程師、數據科學家和雲端工程師，擁有超過六年的開發和部署數據驅動解決方案的經驗。艾哈邁德專注於構建和微調機器學習模型，利用先進的深度學習技術，並優化基於雲端的解決方案。他的專業知識延伸至雲端工程和DevOps實踐，擅長設計和實施可擴展、高效的雲端架構以及自動化部署過程。
擁有AWS雲端環境的實務經驗和強大的雲端工具背景，艾哈邁德擅長將人工智慧與雲端技術整合，創造穩健的生產就緒解決方案。他在各行各業中推動有影響力的結果，從零售和房地產到健身和企業應用，均有卓越的表現。
艾哈邁德致力於持續學習和成長，始終尋求在人工智慧、數據科學和雲端工程領域產生重大影響。他的職業生涯反映了他對推進技術、優化雲端基礎設施以及通過數據驅動策略和尖端技術促進創新的承諾。
