Cloud-Native Evaluation-as-a-Service: A Microservices Architecture for Scalable AI Monitoring with Conformal Guarantees
EaaS is a cloud-native architecture that operationalizes AI evaluation as six Kubernetes microservices, validated for conformal prediction, drift detection, and fairness monitoring with strong performance.
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[Submitted on 4 Jul 2026]
Title:Cloud-Native Evaluation-as-a-Service: A Microservices Architecture for Scalable AI Monitoring with Conformal Guarantees
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Abstract: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.
Comments: 23 pages, 15 figures, 12 tables
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2607.21623 [cs.LG]
(or arXiv:2607.21623v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.21623
arXiv-issued DOI via DataCite
Submission history
From: Lei Yang [view email] [v1] Sat, 4 Jul 2026 01:39:59 UTC (2,069 KB)
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