Abstract: In one embodiment, a method includes forming a diagnostic model using machine learning by ingesting trusted operational data. The method determines that a predictive data produced by an evaluated model for a period of time does not match a known trusted data for the period of time. The method analyzes whether the predictive data produced by the evaluated model for the period of time is an optimal result of the evaluated model. A determination is made that a mistake occurred when the predictive data is not the optimal result of the evaluated model. A cause of the mistake is explained using the diagnostic model. Lastly, fine-tuning the diagnostic model is performed based on learnings from a past predictive data for different periods of time when compared with past known trusted data for the different periods of time using a processor and a memory.

Abstract: At least one system and method herein are for camera handling in live environments. This includes determining a musical score to be performed in the live environment, analyzing a live version of the musical score with the recorded version of the musical score in a live environment, where the analysis is to determine a time-based difference of at least one frame in the live version against the recorded version, and where the time-based difference is to be used, along with at least one predominant effect of the recorded version or the live version, to enable camera selection or camera adjustments of one or more cameras in the live environment.