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

Machine Learning as a Service (MLaaS) is a powerful cloud paradigm enabling data-driven intelligent applications in Internet of Things (IoT) environments, widely adopted across healthcare, smart homes, and industry due to its cost-effectiveness. However, the dynamic nature of IoT frequently alters data distributions, affecting MLaaS stability, while periodic MLaaS updates further introduce performance drift. Unlike traditional ML systems, MLaaS clients operate as black-box users without access to internal data or parameters, making drift detection particularly challenging. To address this, we propose a novel MLaaS Performance Drift Detection framework for IoT environments. The framework first employs an MLaaS extraction model that learns service behavior from input-output pairs and identifies prediction-influenced features. Building on this, the proposed MLaaS Performance Drift Detection (MPDD) model jointly captures variations in input data and MLaaS behavior. We further design an Adaptive-Temporal Performance Drift Detection Mechanism (APDDM) that dynamically adjusts monitoring frequency based on behavioral and data variations, enabling timely drift detection for effective service management. Extensive experiments on real-world datasets demonstrate that MPDD achieves up to 22-25% accuracy improvement over baseline drift detection methods. APDDM provides an average accuracy gain of approximately 4% and reduces the miss detection rate by around 9% compared to fixed-interval monitoring.

Cloud-Native Evaluation-as-a-Service: A Microservices Architecture for Scalable AI Monitoring with Conformal Guarantees

We present EaaS, a cloud-native reference architecture that operationalizes AI evaluation methods as six stateless Kubernetes microservices: conformal prediction with finite-sample-corrected Adaptive Prediction Sets, calibration assessment, drift detection via RFF-approximated Maximum Mean Discrepancy, fairness monitoring with bootstrap confidence intervals, a DAG-based pipeline orchestrator, and a result storage API. We validate four key methodological concerns. First, empirical coverage is consistent with the marginal conformal guarantee across K=50 random calibration/test splits, with mean coverage within 1.4 percentage points of the nominal target. Second, all four MMLU answer tokens appear in the top-20 logprobs with 0% imputation needed, and simulated imputation at 10% produces less than 1.5% coverage impact. Third, RFF-MMD achieves 100% detection power for mild and severe drift at the median heuristic bandwidth, with Type I error between 5-8.5%. Fourth, fairness monitoring on the UCI Adult Income dataset reveals significant demographic parity disparities by race (DP gap=0.33) with stable alerts across sequential batches. Conformal prediction and calibration services achieve sub-2ms p99 latency at batch size 100; RFF-MMD requires ~500ms suited for periodic batch monitoring. A comparison with four open-source tools suggests that, to the best of our knowledge, no current platform combines conformal-prediction-as-a-service, microservice decomposition, and DAG-based orchestration.