Runtime Monitoring Frameworks for ML Model Reliability

Abstract

Ensuring the reliability of machine learning models during real-time deployment is essential,
particularly in environments where data distributions evolve and operational decisions must remain
consistent. This study proposes a runtime monitoring framework that evaluates model reliability using
internal representation stability, prediction certainty metrics, and temporal output consistency rather
than relying solely on accuracy-based validation. The framework integrates adaptive thresholding and
drift-sensitive recalibration to distinguish between natural variation and meaningful performance
degradation. Experimental evaluations across stable, gradually shifting, and abruptly changing input
conditions show that the framework detects reliability loss significantly earlier than output-level
monitoring alone. Furthermore, the system’s controlled failover routing enables continuous service
delivery while preventing erroneous predictions from influencing downstream processes. The results
demonstrate that effective ML reliability monitoring is inherently dynamic, representation-aware, and
requires operational feedback loops to sustain long-term deployment stability.