Beyond good metrics, models can break silently (vanishing gradients during training), favor some groups unfairly, or stay black boxes. AWS addresses three layers: (1) Debugger watches training health, (2) Clarify measures fairness and explains predictions, (3) error analysis humanly groups failures. Today we distinguish and apply each.
Captures tensors (weights, gradients, loss, activations) during training, auto-detect anomalies via rules.