Abstract A method and server for optimizing hyperparameter tuples for training production-grade artificial intelligence (AI) models. For each one of the AI models, AI model features are extracted and, for the one AI model, an initial distribution of n hyperparameter tuplesis created considering the extracted AI model features therefor. A loop is repeated, until metric parameters are satisfied, comprising: evaluating latency from training the one AI model for each of the n hyperparameters tuples; evaluating model uncertainty from training the one AI model for each of the n hyperparameters tuples; for each of the n hyperparameters tuples, computing a blended quality measurement from the evaluated latency and evaluated model uncertainty; replacing m hyperparameter tuples having the worst blended quality measurements with m newly generated hyperparameter tuples. The metric parameters include one or more of a threshold value on model uncertainty and blended quality measurement gain between successive loops. CA 3070908 2020-02-03
METHOD AND SERVER FOR OPTIMIZING HYPERPARAMETER TUPLES FOR TRAINING PRODUCTION-GRADE ARTIFICIAL INTELLIGENCE (AI)