Léo Dreyfus-Schmidt explains one of the fundamental challenges of deploying machine learning in the real world: the world doesn’t stay still. Using fraud detection as an example, Léo explores: • How fraudsters adapt once existing patterns are detected • Why relationships within your data change over time • How data drift can cause previously successful models to lose accuracy • Why production models need to account for changing behavior