How do you navigate the overwhelming factor zoo of quantitative investing strategies? Which factors actually deliver alpha, and how do you combine them effectively? As machine learning transforms Wall Street, quantitative investors are being squeezed by increasingly loud demands to deliver returns and a vanishingly small tolerance for error. Understanding which systematic strategies work, when they work, and how to implement them has become essential for all contemporary investors. In The Quantamental Revolution: Factor Investing in the Age of Machine Learning, veteran quant and strategist Milind Sharma delivers a comprehensive guide to modern factor investing informed by three decades of Wall Street experience. Drawing on both industry expertise and academic rigor, Sharma shows readers how to tame the factor zoo through machine learning ensembles and smart beta strategies that actually work. The book provides a practical blueprint for implementing superior multi-factor models, explaining the fundamentals of Fama-French and how to implement cutting-edge ML techniques for monetizing alpha in today's hedge fund world. The book systematically covers Enhanced Smart Betas, regime-dependent factor behavior, factor timing versus tilting, style analysis, sector rotation, and the integration of alternative data with NLP sentiment. Readers will discover QMIT's proven methodology backed by over five years of live performance data, including novel approaches to portfolio construction, risk management, and crash protection. The author combines rigorous quantitative frameworks with engaging anecdotal vignettes from his three-decade journey through the evolution of quantamental investing, offering insights not found in academic sources. Perfect for early-career quantitative investors and analysts, traders, market data professionals, and portfolio managers seeking to improve their investment processes, The Quantamental Revolution connects academic finance with practical implementation. A battle-tested framework for generating alpha in the age of AI, the book combines a comprehensive factor library, practical use cases, and real-world implementation details to create an operational manual for quantitative trading.