ML A/B Testing Framework — Champion-Challenger Model Comparison

I will build an ML A/B testing framework for your model deployment — enabling you to run controlled experiments comparing your current production model (champion) against a new candidate model (challenger), with traffic splitting, metric collection, statistical significance testing, and an automated promotion decision when the challenger demonstrates statistically significant improvement. Replacing a production ML model without A/B testing means either deploying a potentially worse model to all users simultaneously or running uncontrolled experiments that cannot distinguish genuine model improvement from natural performance variation.

The framework covers traffic splitting configuration between champion and challenger, prediction logging for both models, business metric collection aligned to model decisions, statistical significance testing (t-test or Bayesian A/B), a results dashboard showing real-time experiment progress, and an automated or manual promotion workflow.