Automated Machine Learning for Data-centric Systems: Knowledge-Guided and Experience-Driven Approaches (Big Data Management)

Automated Machine Learning for Data-centric Systems provides a system-oriented and knowledge-driven perspective on automated machine learning in modern data-centric environments.As machine learning models become core components of data management systems, the manual design and optimization of models increasingly limit scalability, reproducibility, and long-term adaptability.This book addresses these challenges by rethinking AutoML not merely as a collection of optimization algorithms, but as a foundational capability embedded within data-centric systems. The book presents a unified framework that connects core AutoML techniques-such as hyperparameter optimization, combined algorithm selection and configuration, neural architecture search, and model compression-with system-level considerations and diverse data scenarios.It emphasizes how knowledge, experience, and structural properties of data can guide automation, enabling AutoML systems to move beyond blind search toward more efficien