Chapter 1: Introduction to Scalable AI Systems -- Chapter 2: Fundamentals of Scalability in AI -- Chapter 3: Data Engineering for Scalable AI -- Chapter 4: Scalable AI Algorithms and Models -- Chapter 5: Scalable AI Infrastructure and Architecture -- Chapter 6: Scalable AI Deployment and Productionization -- Chapter 7: Scalable AI for Real-time and Streaming Data -- Chapter 8: Scalable AI for Edge Computing -- Chapter 9: Scalable AI Governance and Ethics -- Chapter 10: Case Studies and Best Practices -- Chapter 11: Future Trends and Emerging Technologies -- Chapter 12: Conclusion and Final Thoughts.

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.