During an audit-readiness phase, a fintech company's MLOps team must prove that a freshly retrained credit-risk model behaves as expected when exposed to live production traffic. Regulatory policy forbids the candidate model from influencing any real lending decisions until its calibration and fairness have been verified under real-world loads. Engineers also want to capture latency metrics and log the divergence between the new model's predictions and those of the incumbent model-without adding delay for customers. Which validation approach best satisfies these requirements and follows MLOps best practices?
Shadow deployment that mirrors all production requests to the new model and logs its predictions for later analysis.
Offline hold-out validation using a static 30 % test split from historical loan applications.
Blue/green deployment that routes 100 % of traffic to a new environment after initial smoke tests.
Canary release that gradually shifts a small percentage of live lending decisions to the new model while monitoring KPIs.
Shadow (or mirrored-traffic) deployment feeds a copy of every production request to the candidate model in parallel with the champion model. Only the champion's output is returned to users, so no customer decision is affected and regulatory constraints are met. Because both models receive identical inputs, the team can compare prediction deltas, compute real-time latency statistics, and evaluate fairness or calibration before any traffic is switched. Blue/green deployment swaps all traffic once basic tests pass, so it violates the 'no decision impact' rule. A canary release does send a small share of real lending decisions to the new model, which still breaks the regulatory restriction. Offline hold-out validation runs only on historical data and cannot reveal performance or latency issues that emerge under current production traffic.
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