Deep learning models often look impressive during training and validation high accuracy, stable loss curves, and strong benchmark results. But once they meet real users and live data, cracks start to appear. Inputs become noisier, edge cases show up more often than expected, and data distributions quietly drift away from what the model learned. Performance doesn’t always collapse overnight; instead, it degrades slowly, making the problem harder to notice and even harder to explain to stakeholders.