Data Quality and Real World Constraints Questions

Addresses how to work with imperfect real world data and operational constraints. Topics include diagnosing and handling missing data and outliers, dealing with label noise and class imbalance, detecting and reacting to data drift, designing robust features and sampling strategies, ensuring data provenance and lineage, instrumentation for reliable signal collection, and making trade offs given latency, privacy, or cost constraints.