Automated concept drift detection in live machine learning systems for machine learning model updating

Abstract

A machine learning (ML) system and methods are provided that are configured to detect concept drift in ML models. The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform drift detection operations which include receiving a first data set for use during online training of a first ML model, determining a change to an uncertainty bound metric associated with classifiers for features utilized by the first ML model, identifying that the first data set causes the concept drift with the online training of the first ML model, determining characterization information about a type of the concept drift, generating an ML update paradigm based on the concept drift and the characterization information, alerting an ML model updater of the ML update paradigm.