Continuous integration and automated testing of machine learning models

Abstract

A system and method are disclosed to generate, modify, and deploy machine learning models. Embodiments include a database comprising historical sales data and a server comprising a processor and memory. Embodiments receive historical sales data comprising aggregated sales data for one or more items sold in one or more stores over one or more past time periods. Embodiments train a first machine learning model to learn model parameters and generate sales predictions by identifying one or more causal factors that influence the sale of one or more items. Embodiments train a second machine learning model, based on the first machine learning model, to generate second predictions. Embodiments evaluate the predictions of the first and second machine learning models as compared to the historical sales data, and deploy the machine learning model that generated the predictions that are closer to the historical sales data to generate one or more subsequent predictions.