Abstract: Techniques for machine and deep learning process modeling of performance and behavioral data are described, including receiving objective performance data, including an induction document, and raw performance data, validating the raw performance data, determining whether incumbent data is sufficient to build a model, building a model to generate an output performance dataset, evaluating a behavioral dataset generated using behavioral attributes determined from a survey, identifying a candidate file using the model, the model being identified as a model candidate, and evaluating the model candidate against one or more other model candidates using one or more exit criteria to determine whether the model candidate, relative to the one or more other model candidates, is used to identify a release candidate.

Abstract: Techniques for machine and deep learning process modeling of performance and behavioral data are described, including receiving objective performance data, including an induction document, and raw performance data, validating the raw performance data, determining whether incumbent data is sufficient to build a model, building a model to generate an output performance dataset, evaluating a behavioral dataset generated using behavioral attributes determined from a survey, identifying a candidate file using the model, the model being identified as a model candidate, and evaluating the model candidate against one or more other model candidates using one or more exit criteria to determine whether the model candidate, relative to the one or more other model candidates, is used to identify a release candidate.