run-ab-test-models

Execute controlled experiments comparing model versions using traffic splitting and statistical analysis.

Define test parameters, success criteria, and statistical requirements.

Expected: Experiment configuration with statistically sound sample size calculation, typically 5-10k samples per variant for 5-10% MDE.

Design and execute A/B tests for ML models in production using traffic splitting, statistical significance testing, and canary/shadow deployment strategies. Measure performance differences and make data-driven decisions about model rollout. Use when validating a new model version before full rollout, comparing candidate models trained with different algorithms, measuring business metric impact of model changes, or when regulatory requirements mandate gradual rollout. Source: pjt222/development-guides.