AI Engineering: Building Intelligent Systems

AI Engineering: Building Real-World Intelligent Systems is written for practitioners who want to move beyond demos, notebooks, and prototypes—and learn how to design, build, and operate AI systems that work in the real world.This book takes a deliberate engineering-first approach to artificial intelligence. Instead of starting with heavy mathematics or abstract theory, it focuses on the foundations that matter in practice and the decisions engineers must make when turning AI capabilities into reliable, scalable systems. Readers are guided from core technical fundamentals into the architecture, design patterns, and trade-offs required to build intelligent systems that can survive real-world constraints.The book is organized into three major themes. It begins with foundations, offering a practical refresher on Python, statistics, and linear algebra—just enough to ground readers without overwhelming them. It then moves into building intelligent systems, covering modern AI concepts such as machine learning, large language models, retrieval-augmented generation, prompt engineering, fine-tuning, agentic workflows, and system orchestration. Throughout, the emphasis remains on implementation, integration, and engineering judgment rather than academic depth. Finally, the book addresses the AI roadmap, helping readers think strategically about adoption, scaling, governance, cost, reliability, and long-term sustainability of AI systems in organizations.Unlike many AI books that focus narrowly on algorithms or tools, this book treats AI as a system discipline. It highlights real-world considerations such as data quality, system boundaries, operational risks, and production readiness. Practical examples, engineering patterns, and decision frameworks are used to connect concepts to execution.This book is intended for AI engineers, software engineers, architects, product leaders, and technologists who want to build systems that last—not just models that impress. Read more