Applied AI Engineering for Developers: Building Intelligent Applications at Scale
Keywords:
Applied Artificial Intelligence, AI Engineering, Explainable AI (XAI), Wissira Press Academic Books, Books by Wissira, Wissira Research LabSynopsis
Artificial Intelligence is no longer a distant vision confined to research laboratories-it is now a fundamental force shaping modern software systems. From recommendation engines and intelligent assistants to predictive analytics and autonomous systems, AI has become deeply integrated into the applications developers build every day. This transformation has created a growing need for professionals who not only understand algorithms but can also engineer scalable, reliable, and ethical AI-driven solutions. This book, Applied AI Engineering for Developers: Building Intelligent Applications at Scale, is written to address that need.
The purpose of this book is to bridge the gap between theoretical knowledge and practical implementation. While many resources focus either on mathematical foundations or high-level concepts, developers often require a more applied perspective-one that explains how to take models from experimentation to production. This book emphasizes that journey. It explores how data is prepared, how models are selected and trained, how systems are designed, and how AI solutions are deployed and maintained in real-world environments.
Throughout the chapters, the reader is guided step by step through the lifecycle of AI engineering. Beginning with foundational concepts, the book gradually moves into more advanced topics such as deep learning architectures, scalable infrastructure, and deployment strategies. Special attention is given to practical challenges faced by developers, including handling imperfect data, optimizing performance, integrating models into applications, and ensuring system reliability at scale. The goal is to equip readers with both conceptual clarity and actionable knowledge.
Equally important is the focus on responsibility and ethics. As AI systems increasingly influence decisions that affect individuals and societies, developers carry a significant responsibility. This book highlights the importance of fairness, transparency, and data privacy, encouraging readers to build systems that are not only powerful but also trustworthy and accountable.
This work is intended for software developers, data engineers, and technology enthusiasts who wish to expand their skills into the domain of applied AI. Whether you are beginning your journey or looking to strengthen your expertise, the content is designed to be accessible while still offering depth. Real-world examples and case-based discussions are included to make concepts more relatable and easier to apply.
The field of AI is evolving rapidly, and no single book can capture its entirety. However, the aim here is to provide a strong foundation and a practical framework that will remain relevant as technologies continue to advance. Readers are encouraged to explore further, experiment continuously, and adapt to new developments in this dynamic landscape.
Ultimately, this book is about empowering developers to move beyond using AI as a tool and toward engineering intelligent systems that create meaningful impact. The future of software lies in intelligent, adaptive, and scalable applications-and the ability to build them will define the next generation of innovation.
Chapters
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Chapter 1 Foundations of Applied AI Engineering
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Chapter 2 Data Engineering for AI Systems
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Chapter 3 Machine Learning Models and Selection
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Chapter 4 Deep Learning and Neural Architectures
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Chapter 5 Building AI-Powered Applications
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Chapter 6 Scalable AI Infrastructure and Deployment
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Chapter 7 Responsible AI and Ethics
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Chapter 8 Performance Optimization and Monitoring
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Chapter 9 Future Trends and Advanced AI Applications
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References
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