MDB: Interactively Querying Datasets and Models
As models are trained and deployed, developers need to be able to
systematically debug errors that emerge in the machine learning pipeline. We
present MDB, a debugging framework for interactively querying datasets and
models. MDB integrates functional programming with relational algebra to build
expressive queries over a database of datasets and model predictions. Queries
are reusable and easily modified, enabling debuggers to rapidly iterate and
refine queries to discover and characterize errors and model behaviors. We
evaluate MDB on object detection, bias discovery, image classification, and
data imputation tasks across self-driving videos, large language models, and
medical records. Our experiments show that MDB enables up to 10x faster and
40\% shorter queries than other baselines. In a user study, we find developers
can successfully construct complex queries that describe errors of machine
learning models.