Deploying TensorFlow-Based Risk Assessment Models for High-Stakes Operational Decisions in Regulated Enterprise Systems: An Empirical Study of Lifecycle, Serving, and Drift Governance
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I2P120Keywords:
TensorFlow Serving, Risk Assessment, Model Drift, Calibration, Fairness Audit, MLOpsAbstract
Risk assessment models increasingly mediate consequential operational decisions in regulated enterprise environments, where accountability, auditability, and fairness constraints amplify the cost of silent model failure. This paper presents an empirical study of deployment patterns for TensorFlow-based risk-assessment models, synthesizing observations from the production deployments we examined across regulated enterprise operational systems. We describe a reference lifecycle that spans the feature pipeline, training discipline, validation protocol, TensorFlow Serving inference architecture, drift detection regime, and fairness audit cadence, and we evaluate each stage against criteria observed to matter most for high-stakes operational use. The measurement protocol couples temporally split holdout evaluation with calibration analysis, p95 inference latency measurement under autoscaling, and a quarterly fairness audit on illustrative protected groupings. Across the reference deployments, the production model achieved an observed AUC of 0.85, a Brier score of 0.12, an expected calibration error of 0.02, and a p95 inference latency of 96 ms under representative load. Illustrative fairness metrics across three protected groupings remained within a demographic-parity gap of 0.06 and an equal-opportunity gap of 0.04 after recalibration. We discuss how training-serving symmetry, calibrated outputs, and continuous drift monitoring jointly determine whether risk-assessment models retain operational trust over time, with implications for the broader field of trustworthy machine learning in regulated decision support.
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