Manifold is a model-agnostic visual debugger for machine learning (ML). Model developers spend a significant amount of time on iterating and improving an existing model rather than creating a new one. By shifting the focus from model space to data space, Manifold supplements the existing performance metrics with a visual characteristics of the data set that influences the model performance. We think Manifold will be a useful tool to assess in the ML ecosystem.