What is Model Drift?

Model Drift refers to the degradation of a machine learning model's predictive performance over time. It manifests as data drift (changes in input data distribution) or concept drift (changes in the relationship between inputs and outputs). Continuous monitoring is essential to detect drift and trigger retraining.

For African enterprises, model drift is particularly challenging in dynamic markets where economic conditions, customer behaviour, and regulatory requirements change rapidly. Automated drift detection ensures AI systems remain accurate and compliant throughout their lifecycle.