Active learning drift analysis and training

A computer-implemented method, comprising:

• selecting, by a system operatively coupled to a processor, an ensemble of deep learning regressors respectively comprising one or more deep learning models, wherein the one or more deep learning models are trained and deployed in a computing environment accessible to users;
• identifying, by the system, that the ensemble is experiencing drift, wherein identifying that the ensemble is experiencing the drift comprises determining that a majority of the deep learning regressors of the ensemble have the drift;
• analyzing, by the system, uncertainty samplings from the ensemble to determine a time period when the drift occurred, wherein the determining the time period comprises:
• identifying, as a beginning of the time period, an earliest time instance when any one deep learning regressor of the majority started experiencing the drift, and
• identifying, as an ending time of the time period, a latest time instance when any other deep learning regressor of the majority started experiencing the drift;
• and
• retraining, by the system, at least one deep learning model of at least one of the deep learning regressors of the majority based on the drift and the time period when the drift occurred.

A computer program product facilitating a process to train learning models based on determined drift, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

• select, by the processor, an ensemble of deep learning regressors respectively comprising one or more deep learning models, wherein the one or more deep learning models are trained and deployed in a computing environment accessible to users;
• identify, by the processor, that the ensemble is experiencing drift, wherein identifying that the ensemble is experiencing the drift comprises determining that a majority of the deep learning regressors of the ensemble have the drift;
• analyze, by the processor, uncertainty samplings from the ensemble to determine a time period when the drift occurred, wherein the determining the time period comprises:
• identifying, as a beginning of the time period, an earliest time instance when any one deep learning regressor of the majority started experiencing the drift, and
• identifying, as an ending time of the time period, a latest time instance when any other deep learning regressor of the majority started experiencing the drift;
• and
• retrain, by the processor, at least one deep learning model of at least one of the deep learning regressors of the majority based on the drift and the time period when the drift occurred.