Scalable AI and Design Patterns: Design, Develop, and Deploy Scalable AI Solutions

Understand and apply the design patterns outlined in this book to design, develop, and deploy scalable AI solutions that meet your organization's needs and drive innovation in the era of intelligent automation.

This book begins with an overview of scalable AI systems and the importance of design patterns in creating robust intelligent solutions. It covers fundamental concepts and techniques for achieving scalability in AI systems, including data engineering practices and strategies. The book also addresses scalable algorithms, models, infrastructure, and architecture considerations. Additionally, it discusses deployment, productionization, real-time and streaming data, edge computing, governance, and ethics in scalable AI. Real-world case studies and best practices are presented, along with insights into future trends and emerging technologies.

The book focuses on scalable AI and design patterns, providing an understanding of the challenges involved in developing AI systems that can handle large amounts of data, complex algorithms, and real-time processing. By exploring scalability, you will be empowered to design and implement AI solutions that can adapt to changing data requirements.

What You Will Learn

• Develop scalable AI systems that can handle large volumes of data, complex algorithms, and real-time processing
• Know the significance of design patterns in creating robust intelligent solutions
• Understand scalable algorithms and models to handle extensive data and computing requirements and build scalable AI systems
• Be aware of the ethical implications of scalable AI systems

Who This Book Is For

AI practitioners, data scientists, and software engineers with intermediate-level AI knowledge and experience

本書旨在介紹設計模式，並將其應用於設計、開發和部署可擴展的人工智慧解決方案，以滿足組織的需求並推動智能自動化時代的創新。本書首先概述了可擴展的人工智慧系統以及在創建強大智能解決方案時設計模式的重要性。它涵蓋了實現人工智慧系統可擴展性的基本概念和技術，包括數據工程實踐和策略。本書還討論了可擴展的算法、模型、基礎設施和架構考慮因素。此外，它還討論了部署、生產化、實時和流式數據、邊緣計算、治理以及可擴展人工智慧中的倫理問題。書中還提供了真實案例研究和最佳實踐，以及對未來趨勢和新興技術的見解。

本書專注於可擴展的人工智慧和設計模式，旨在讓讀者了解開發能夠處理大量數據、複雜算法和實時處理的人工智慧系統所面臨的挑戰。通過探索可擴展性，讀者將能夠設計和實施能夠適應不斷變化的數據需求的人工智慧解決方案。

本書的學習重點包括：
- 開發能夠處理大量數據、複雜算法和實時處理的可擴展人工智慧系統
- 了解設計模式在創建強大智能解決方案中的重要性
- 理解可擴展算法和模型，以處理大量數據和計算需求，並構建可擴展的人工智慧系統
- 了解可擴展人工智慧系統的倫理問題

本書適合具有中級人工智慧知識和經驗的人工智慧從業者、數據科學家和軟件工程師閱讀。

Abhishek Mishra is a seasoned data science and analytics professional with extensive expertise in information technology services and products. With a remarkable 15-year track record, Abhishek has leveraged his data science knowledge and engineering skills to analyze vast datasets, interpret findings, and communicate actionable insights to drive effective business decision making. He has successfully led global analytics teams, developed cutting-edge machine learning solutions, and formulated data analytics strategies for esteemed organizations such as Marsh and McLennan, Fiserv India Pvt Ltd, and Deloitte Inc.

Abhishek Mishra 是一位經驗豐富的數據科學和分析專家，專注於資訊技術服務和產品。憑藉著15年的卓越成績，Abhishek運用他的數據科學知識和工程技能來分析大量數據集，解釋發現並傳達可行的見解，以推動有效的業務決策。他成功領導全球分析團隊，開發尖端的機器學習解決方案，並為Marsh and McLennan、Fiserv India Pvt Ltd和Deloitte Inc等知名組織制定數據分析策略。