Data Drift vs. Concept Drift: What Is the Difference? - DATAVERSITY

Model drift refers to the phenomenon that occurs when the performance of a machine learning model degrades with time. This happens for various reasons, including data distribution changes, changes in the goals or objectives of the model, or changes to the environment in which the model is operating. There are two main types of model drift that can occur: data drift and concept drift. Data drift refers to the changing distribution of the data to which the model is applied. Concept drift refers to a changing underlying goal or objective for the model.