CONSTRAINT-BASED OPTIMIZATION OF MACHINE LEARNING MODELS

Abstract: methods and apparatuses for constraint-based optimization of machine learning classification models include determining performance constraints associated with deployment and execution of a model and identifying candidate pipelines. for each candidate pipeline, a model is trained using a training dataset, the trained model is executed using a testing dataset to determine performance characteristics for the trained model, and the performance characteristics are compared to the performance constraints. one of the candidate model pipelines that meets the performance constraints is identified and a production model is built based upon the identified candidate pipeline. the production model is deployed to a production computing environment for execution.

Inventor(s): Saloni Priyani, Nikhil Krishnegowda, Samir Kakkar

CPC Classification: G06N20/00 (Machine learning)

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