Making predictions in financial markets is challenging because the price formation mechanism can change from time to time, in a process academics call a “concept drift”. We have implemented a unique machine learning approach to making short-term price predictions for corporate bonds, specifically designed to maintain high prediction accuracy across market regimes, issuer credit quality and instrument life cycles. Our commercially available engine BondDroid TM is currently used by several large buy-side and sell-side institutions. BondDroid TM predicts bid and ask prices for thousands of corporate bonds in real-time. The problem of identifying and automatically reacting to a regime change is not unique to financial markets. So, our conclusions are useful and applicable beyond finance.