Frameworks for Runtime Reliability Assessment in Deployed Machine Learning Systems

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.