Drift Detection

The conceptual idea of drift is that as deployed artificial intelligence (AI) systems adapt to evolving data streams, the predictive power may potentially degrade. Their inferences may “drift” away from the intended targets. When using semantic data models, it is possible to ensure that data are consistently monitored for drift, thus maintaining the performance of AI systems.

Please contact the team for more information.