Why do TensorFlow inference systems require observability beyond accuracy metrics?

Because production systems require monitoring latency, drift, throughput, and system health, not just accuracy.

Accuracy alone does not reflect production health. TensorFlow inference systems must monitor latency distributions, error rates, input feature drift, prediction confidence, and system-level metrics like CPU/GPU utilization. These signals help detect degradation even when labels are unavailable or delayed.