Data Quality

Covariate shift/label shift/concept drift/feature change/label schema change taxonomy; degenerate feedback loops (mechanism, detection, correction via randomisation and positional features); detection methods (KS test, two-sample tests, feature validation, prediction monitoring, sliding vs cumulative statistics); proactive design; retraining strategies

Data-centric AI; three criteria (quality, coverage, quantity); acquisition and annotation; data synthesis (rule-based, simulation, AI-powered, reverse instruction); model distillation; model collapse; data processing pipeline