This is a very relevant question and something many teams encounter early on.

In most cases, the gap comes from the difference between controlled evaluation environments and the variability of real-world inputs. Test datasets are often cleaner, more structured, and aligned with expected patterns, while production data is noisy, ambiguous, and constantly evolving.

Another factor is feedback loops. In testing, performance is measured against known outcomes, but in production, failures are often silent and harder to detect. Without strong monitoring and continuous evaluation, models degrade without immediate visibility.

The real shift happens when teams start treating models as systems rather than static artifacts, with emphasis on data quality, observability, and iteration.