Data Engineering for Artificial Intelligence and ML

Data Engineering for Artificial Intelligence provides a structured and in-depth exploration of the technologies, architectures, and methodologies that power AI systems. The book begins by establishing core foundations of data engineering, including data lifecycle management, storage architectures, distributed computing, and ETL/ELT frameworks. It then progresses toward advanced topics such as big data processing, real-time streaming systems, feature stores, data quality frameworks, and scalable cloud-native architectures.
A strong emphasis is placed on integrating data engineering with machine learning workflows. Readers will gain insights into building reliable data pipelines, managing large-scale datasets, ensuring privacy and compliance, and implementing monitoring and observability for AI systems. The book also explores emerging trends such as data mesh, lakehouse architectures, MLOps integration, and AI-driven data optimization.
With detailed explanations, structured subsections, and practical perspectives, this book serves as both an academic reference and a professional guide. It equips readers with the knowledge required to design, implement, and manage high-performance data systems that fuel modern AI applications. By connecting theory with practice, this book empowers readers to build scalable, secure, and future-ready AI data infrastructures.