ab-test-framework-ml

A/B testing for machine learning models in production requires a framework for experiment design, sample size calculation, traffic splitting, and statistical analysis to evaluate model performance before full rollout.

To calculate sample size for an ML A/B test, you need the baseline conversion rate and the minimum detectable effect. This framework computes the required sample size using libraries like numpy and statsmodels to ensure statistical rigor.

Yes, this A/B testing framework supports Bayesian analysis using pymc3 and arviz, alongside frequentist methods. It also monitors production models for concept drift and performance degradation over time.

Yes, you can use this framework to monitor inference latency during machine learning experimentation. It explicitly addresses ML-specific production challenges like concept drift and inference latency alongside performance tracking.

The best way to compare a new recommendation engine against the current model is using an ML-specific A/B testing framework. It handles traffic splitting and statistical analysis to ensure improvements in metrics like user engagement are statistically significant.