Practical AI Engineering: Building Scalable, Reliable, Real-World Systems
Artificial intelligence has moved beyond experimentation. Today, organizations face a more difficult challenge: building AI systems that are reliable, scalable, maintainable, and capable of operating in complex real-world environments. Practical AI Engineering is a comprehensive, original guide designed to meet this challenge head-on.
Written for engineers, AI architects, system designers, and technically minded leaders, this book presents a system-level approach to AI engineering, focusing on the full lifecycle of production-ready AI systems. Rather than emphasizing algorithms or code, it explores the architectural, operational, and strategic decisions required to design AI systems that perform consistently under real-world constraints.
The book guides readers from foundational principles to advanced system engineering topics, covering problem definition, system architecture, data pipelines, model selection, deployment strategies, monitoring, scaling, optimization, and long-term maintenance. It addresses the realities of production AI, including reliability, performance trade-offs, observability, lifecycle management, and integration with existing enterprise systems.
Through conceptual frameworks, real-world scenarios, and illustrative case studies, Practical AI Engineering bridges the gap between theory and deployment. It also examines human-centered design, governance, ethics, and compliance, helping readers build AI systems that are not only powerful, but trustworthy and sustainable.
The book concludes with forward-looking insights into emerging trends such as edge AI, federated learning, and hybrid human-AI systems, offering strategies to future-proof AI solutions in a rapidly evolving technological landscape.
Clear, authoritative, and practical, this book serves as a definitive manual for anyone responsible for designing, deploying, or maintaining real-world AI systems at scale.
