Concept drift, also known as model drift, occurs when the task that the model was designed to perform changes over time. For example, imagine that a machine learning model was trained to detect spam emails based on the content of the email. If the types of spam emails that people receive change significantly, the model may no longer be able to accurately detect spam. References: Understanding Data Drift and Model Drift: Drift Detection in Python | DataCamp, Machine Learning Monitoring, Part 5: Why You Should Care About Data and Concept Drift