Distribution Shift

A change in the statistical properties of data between the training environment and the deployment environment. When a model encounters inputs that look different from what it was trained on, its performance can degrade unpredictably. Distribution shift is one of the most common causes of real-world AI failures and motivates careful evaluation across diverse conditions before deployment.
See also: data drift, concept drift, overfitting.