You are part of your organization’s ML engineering team and notice that the accuracy of a model that was recently deployed into production is deteriorating. What is the best first step address this?

When a production model’s predictive quality slips, the recommended first action is to run a champion-/challenger evaluation. A newly trained “challenger” model is executed in parallel (often in shadow or A/B mode) against the same live data as the current “champion.” This immediately confirms whether the degradation is caused by model aging, data drift, or upstream data problems, while containing operational risk because the challenger’s outputs need not affect users until it demonstrably outperforms the champion. Only after objective side-by-side metrics are collected should rollback, red-teaming, or full audits be considered.

A. Replacing with an older version is blind rollback; the previous model may be equally stale and offers no evidence-based diagnosis of drift or data issues.
C. A full audit is broader (fairness, compliance, documentation) and time-intensive; it should follow, not precede, quick performance validation via champion/challenger tests.
D. Red-teaming probes security and adversarial robustness, not routine accuracy deterioration caused by data or concept drift.

1. Google Cloud

Vertex AI MLOps: “Champion-Challenger Pattern

” Continuous Evaluation section

para 3-5 (accessed 2025-10-30).

2. Microsoft Azure

“MLOps v2 – Deployment Patterns

” pp. 12-14

Champion/Challenger Testing (2023).

3. NIST SP 1270

“Towards a Standard for AI Risk Management

” §4.3.2 Continuous Monitoring

p. 22 (2023).

4. Breck

E. et al.

“Data Validation for Machine Learning

” Proceedings of SysML 2019

§6 (discusses shadow/parallel model testing).

5. Parsons

A. “Predictive Maintenance for Machine-Learning Models

” ACM Queue

Vol 18 (5)

pp. 65-66

https://doi.org/10.1145/3464162.3577362.