Data Drift

A change in the statistical properties of the input data a model receives after deployment, compared to the data it was trained on. Even if the underlying task has not changed, data drift can cause model performance to deteriorate and signals that retraining may be needed. Related to, but distinct from, concept drift, which refers to changes in the relationship between inputs and outputs.
See also: concept drift, Model Monitoring, batch learning.