A method and system for optimizing a learning model post-deployment. Specifically, the disclosed method and system re-optimize—i.e., re-train and/or re-validate—machine learning and/or artificial intelligence algorithms that have already been deployed into a production environment. During post-deployment, the re-optimization process may transpire following the advent of varying model re-adjustment triggers.