Core ML Concepts · intermediate · concept 39 of 311 · Updated Nov 4, 2026

✅Data Quality for AI

Data quality for AI means the training and evaluation data is accurate, complete, consistent, current and representative of the cases the model will meet. It matters because a model learns the errors in its data as faithfully as the signal, and an error in a test set makes the benchmark itself wrong. Example: Northcutt et al. estimated at least 3.3 percent label errors across ten benchmark test sets, and at least 6 percent in ImageNet validation.