You can get reliable uncertainty estimates without expensive retraining by using influence functions and linear algebra—making uncertainty quantification practical for real models.
Ribbon is a fast method for measuring how uncertain a machine-learning model's predictions are. Instead of retraining a model many times (which is expensive), it uses math tricks to estimate uncertainty from a single trained model, working well even when the model assumptions are wrong.