Deep learning models often look reliable in training and validation, but real-world deployment exposes weaknesses that weren’t visible in controlled environments. Live data is messier, distributions shift, and edge cases appear more frequently than expected. These issues don’t always cause failures, but they slowly erode model performance while metrics appear stable.

In many cases, the bigger challenge isn’t the model but the ecosystem around it. Data pipelines change, latency constraints surface, feedback loops alter behavior, and monitoring is insufficient to catch early drift. By the time problems are noticed, the model is already misaligned with reality highlighting that production success depends far more on data and systems than on model accuracy alone.