Dynamic data selection for a machine learning model

Abstract

Embodiments implement a machine learning prediction model with dynamic data selection. A number of data predictions generated by a trained machine learning model can be accessed, where the data predictions include corresponding observed data. An accuracy for the machine learning model can be calculated based on the accessed number of data predictions and the corresponding observed data. The accessing and calculating can be iterated using a variable number of data predictions, where the variable number of data predictions is adjusted based on an action taken during a previous iteration, and, when the calculated accuracy fails to meet an accuracy criteria during a given iteration, a training for the machine learning model can be triggered.