RESOURCE-AWARE AND ADAPTIVE ROBUSTNESS AGAINST CONCEPT DRIFT IN MACHINE LEARNING MODELS FOR STREAMING SYSTEMS

摘要

Complex computer system architectures are described for detecting a concept drift of a machine learning model in a production environment, for adaptive optimization of the concept drift detection, for extracting embedded features associated with the concept drift using a shadow learner, and for adaptive adjustment of the machine learning model in production to mitigate the effect of predictive performance drop due to the concept drift.