| CPC G06N 20/00 (2019.01) [G06N 5/046 (2013.01)] | 20 Claims |
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1. A method comprising, by a computing system:
selecting one or more machine learning (ML) models from a plurality of ML models;
determining a common schema for a model group that includes the one or more ML models, wherein the common schema indicates names and datatypes of features of the one or more ML models and each ML model in the model group is configured to perform a same function;
converting a first ML model having a schema different from the common schema to a converted first ML model having the common schema;
adding the converted first ML model to the model group;
dynamically selecting, by a model selector for the model group and based on a set of rules or a trainable selection model, at least one ML model from the model group for data analysis;
analyzing input data using the model group including the at least one dynamically selected ML model, and the model selector;
determining, during the analyzing, a score for the model group including the at least one dynamically selected ML model, and the model selector based on the analyzing and a set of scoring metrics; and
updating, during the analyzing, the model selector or the model group based upon determining that the score is below a threshold value.
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