Extracting sustainable alpha in today's competitive financial markets demands more than just statistical intuition-it requires rigorous, scalable machine learning infrastructure. This book is the definitive guide designed specifically for AI-driven quantitative investors, researchers, and data scientists seeking to bridge the gap between predictive modeling and systematic trading. Whether you are transitioning from traditional factor investing to advanced AI strategies or upgrading to institutional-grade software frameworks, you will find a comprehensive blueprint for engineering robust automated trading systems. At the core of this text is Qlib, a powerful open-source platform tailored for quantitative investment. You will master the end-to-end lifecycle of a systematic strategy, starting with meticulous data acquisition and leakage-safe feature engineering. The book meticulously explores how to design sophisticated machine learning pipelines, validate alpha factors, and evaluate signal quality under strict temporal constraints. You will also learn to translate raw algorithmic predictions into investable portfolios, navigating the practical complexities of transaction costs, turnover control, and high-fidelity backtesting to avoid common simulation illusions. To maximize the value of this text, readers should possess a foundational understanding of Python, financial time-series manipulation, and basic supervised learning principles. Beyond theoretical alpha modeling, this guide distinguishes itself by emphasizing real-world MLOps and production disciplines. You will disc