Monowar Bhuyan

Performance Drift Detection in Machine Learning as a Service (MLaaS) for IoT Environments

This work proposes a novel MLaaS Performance Drift Detection framework for IoT environments that employs an MLaaS extraction model that learns service behavior from input-output pairs and identifies prediction-influenced features, and designs an Adaptive-Temporal Performance Drift Detection Mechanism that dynamically adjusts monitoring frequency based on behavioral and data variations.