A framework for the analysis of polarized exclusive scattering cross sections is described that uses physics constraints from symmetries as well as predictions from lattice QCD to inform machine learning (ML) algorithms. Physics driven and ML based benchmarks are defined for a wide range of deeply virtual exclusive processes. The Compton Form Factors (CFFs), or convolutions of Generalized Parton Distributions (GPDs), are extracted using an uncertainty quantification technique, the random targets method, that allows us to address the separation of aleatoric and epistemic uncertainties in exclusive scattering analyses.