Sustainable retraining for production machine learning

Abstract

A retraining monitoring system maintains the sustainability of a production machine learning (ML) model system that includes a production ML model retraining platform. The retraining monitoring system collects contextual data from the production ML model system and determines if one or more of a currently-selected architectural options has to be changed for sustainability. An architectural option of the production ML model retraining platform, such as, a processing location is selected from a cloud retraining platform or an on-premises retraining platform by a selection process based on a multi-armed bandit problem. An evaluation of the retraining architecture is dealt with as a reinforcement learning problem to implement one of a periodic retraining architecture or a reactive retraining architecture.