Why do TensorFlow pipelines require feature-level monitoring instead of only model-level monitoring?

Because feature drift often causes model failure before overall accuracy degradation is visible.

Feature-level monitoring tracks distribution changes, missing values, and schema shifts at the input level. TensorFlow models depend heavily on input distributions, so even small feature shifts can degrade performance significantly before overall accuracy signals appear. Monitoring features allows earlier detection of system issues.