In predictive analytics and machine learning, the term 'concept drift' means that the properties of the target variables (i.e. the themes and concepts underlying each of the Labels in Re:infer), which the Model is trying to predict, change over time in unforeseen ways.
This causes problems because the predictions become less accurate as time passes and the variables that the Model is trying to predict become increasingly different to the data on which the Model was trained.
Concept drift is one of the key reasons why it's important to properly maintain Models used in production use-cases, e.g. automations.