| CPC G06N 20/00 (2019.01) [G06F 16/2379 (2019.01); G06F 16/2455 (2019.01)] | 22 Claims |
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1. A method, comprising:
receiving a representation of a request;
identifying a set of attributes associated with the request;
updating a first model storage that includes a first plurality of machine learning (ML) models to generate a second model storage that includes a second plurality of ML models, the second plurality of ML models including an ML model that is not included in the first plurality of ML models, the set of attributes represented using a set of state features and the ML model trained using a process that includes:
receiving training data;
validating the training data;
randomizing, after validating the training data, the training data to generate randomized training data; and
training the ML model using the randomized training data;
filtering the second model storage, based on the set of attributes, to identify a subset of ML models included in the second model storage, the subset of ML models including multiple ML models from the second model storage, each ML model from the subset of ML models associated with at least one attribute from the set of attributes; and
causing, to generate an output, the request to be processed using the subset of ML models.
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