Abstract: Techniques for selecting universal hyper parameters for use in a set of machine learning models across multiple computing environments include detection of a triggering condition for tuning a set of universal hyper parameters. The set of universal hyper parameters dictate configuration of the set of machine learning models that are independently executing, respectively, in the multiple computing environments. Based on the detected triggering condition, a first subset of universal hyper parameters from the set of universal hyper parameters are altered to generate a second set of universal hyper parameters. The second set of universal hyper parameters are applied to the set of machine learning models across the multiple computing environments.

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.