How do large-scale TensorFlow systems detect model degradation in production without labels?

They use proxy metrics like prediction distribution shifts, feature drift, and system-level behavioral signals.

In production, true labels are often delayed or unavailable. TensorFlow-based systems rely on surrogate signals such as input feature distribution changes, prediction entropy shifts, confidence score drift, and business KPIs (click-through rate, conversion rate). Statistical tests like KL divergence or PSI (Population Stability Index) are used to detect drift. This allows early detection of degradation before actual ground truth evaluation is available.