Debugging machine learning (ML) models isn’t a walk in the woods. Just ask the data scientists and engineers at Uber, some of whom have the unenviable task of digging into algorithms to diagnose the causes of their performance issues.
To lighten the workload, Uber internally developed Manifold, a model-agnostic visual tool that surfaces the differences in distributions of features (i.e., the measurable properties of the phenomena being observed). It’s a part of the ride-hailing company’s Michelangelo ML platform, where it’s helped various product teams analyze countless AI models. And as of today, it’s available in open source on GitHub.