Edge cases are often underrepresented during training. The model optimizes for majority patterns and lacks exposure to rare scenarios. This is common in NLP, fraud detection, and vision tasks. Augment training data with targeted edge examples and weight them appropriately.

Common mistakes:

1. Assuming edge cases don’t matter

2. Treating all samples equally

3. Not logging failure cases

Production failures usually live at the edges.