What do models know and how? Answering this question requires exploratory analyses comparing many models, but existing techniques are specialized to specific models and analyses. We present memory maps as a general tool to understand a wide range of models by visualizing their sensitivity to data. Memory maps are extensions of residual-leverage plots where the two criteria are modified by easy-to-compute dual parameters obtained by using a Bayesian framework. The new criteria are used to understand a model's memory through a 2D scatter plot where tail regions often contain examples with high prediction-error and variance. All sorts of models can be analyzed this way, including not only those arising in kernel methods, Bayesian methods, and deep learning but also the ones obtained during training. We show use cases of memory maps to diagnose overfitting, compare various models, and analyze training trajectories.