Adaptive optimization of a content item using continuously trained machine learning models

Abstract

A processor receives requests for content items and identifies a first subset of machine learning (ML) models that satisfy a reliability criterion and a second subset of ML models that fail to satisfy the reliability criterion, wherein each ML model is associated with a respective content template and is trained to output a probability that a target associated with an input set of characteristics would perform a target action responsive to being presented with a content item generated based on the respective associated content template. The processing logic assigns each request to either a first group or a second group based on a ratio of a number of ML models in the first subset to a number of ML models in the second subset. For each request in the first group, the processor generates a content item based on a content template associated with the first subset.