Peter Chen's practical guide addresses the rapidly growing space of AI and machine learning applications in investment management, targeting investment professionals who want to understand how these technologies are being deployed by quant funds, robo-advisors, and traditional asset managers and how to integrate them into their own investment processes. The book covers the full workflow of AI-augmented portfolio management: data sourcing and cleaning (including alternative data sources like satellite imagery, credit card transaction data, and social media sentiment), feature engineering, model selection and training, backtesting and avoiding overfitting, portfolio construction incorporating ML-generated signals, and live trading infrastructure. Chen is particularly strong on the practical limitations and pitfalls of machine learning in financial markets, including the relatively short and non-stationary nature of financial time series compared to the training requirements of robust ML models, the multiple testing problem that makes backtested results systematically overstated, and the regime-change problem that causes models trained on historical data to fail during novel market conditions. The book includes case studies of actual ML-augmented investment strategies across equities, fixed income, and alternative assets, with enough technical detail to be genuinely instructive without requiring PhD-level machine learning expertise. A useful practical reference for investment professionals navigating the AI transformation of asset management.