| CPC G06N 20/00 (2019.01) [G06F 18/2193 (2023.01); G06F 18/22 (2023.01)] | 20 Claims |
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1. A machine learning (ML) system configured to detect concept drift in ML models during ML model operations using live data, the ML system comprising:
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 comprise:
receiving a first data set associated with the live data for use during online training of a first ML model of the ML models;
determining a change to an uncertainty bound metric associated with classifiers for features utilized by the first ML model based on the first data set and at least one second data set from previous data utilized with training the first ML model;
identifying that the first data set causes the concept drift with the online training of the first ML model based on the change to the uncertainty bound metric;
determining characterization information about a type of the concept drift based at least on the change to the uncertainty bound metric, wherein the characterization information is associated with the features utilized by the first ML model;
generating an ML update paradigm based on the concept drift of the first ML model and the characterization information; and
alerting an ML model updater of the ML update paradigm.
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