Use for any question or task about checking ML data before or after training, including train-test leakage, data integrity problems, and models far better offline than in production. Runs Deepchecks data integrity, train-test validation and model evaluation suites on tabular, NLP or vision data, reruns them on production samples for drift and gates CI on passed conditions.