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