Modeling spatio-temporal flows is a challenging problem, as dynamic spatio-temporal data possess underlying complex interactions and nonlinearities. Traditional statistical modeling approaches use a data generating process, generally motivated by physical laws or constraints. Deep learning (DL) is a form of machine learning for nonlinear high dimensional data reduction and prediction. It applies layers of hierarchical hidden variables to capture these interactions and nonlinearities without using a data generating process. This talk uses a Bayesian perspective of DL to explain its application to the prediction and uncertainty quantification of spatio-temporal flows from big data. Using examples in traffic flow and high frequency trading, we demonstrate why DL is able to predict sharp discontinuities in spatio-temporal flows. We proceed to discussing the far reaching practical implications of embedding deep spatio-temporal flow predictors into novel actuarial climate models.