All about concept drift in the realm of machine learning. They explain that this happens when the thing a model's trying to predict changes over time unexpectedly, making the model less accurate as the original patterns no longer hold. The texts explore different types of concept drift, like sudden or gradual shifts, and discuss various reasons why it occurs, from changes in the data itself to real-world events. Importantly, they outline methods for spotting concept drift and suggest strategies for dealing with it, such as retraining models or using clever learning techniques to keep them up to date.