What does the term "model drift" refer to in the context of AI?

The term "model drift" specifically refers to changes in data distributions over time, which can significantly impact the performance of an AI model. As the model is initially trained on a specific dataset, any alterations in the input data that occur after the model's deployment can lead to unexpected behavior.

For instance, if the characteristics of the data change due to seasonal effects, economic shifts, or evolving user behaviors, the model may become less accurate or even obsolete, as it is no longer aligned with the new data patterns it encounters. This phenomenon underscores the importance of continuously monitoring and potentially retraining models to adapt to these changes and ensure that they maintain their predictive power.

Understanding model drift is crucial for maintaining model effectiveness and requires strategies including regular assessment, active learning, or retraining to address shifts in data dynamics.