A/B Testing e Sperimentazione

Rigorous experimentation frameworks for evaluating model variants, features, and deployment strategies through controlled A/B tests and multi-armed bandit approaches. We design statistically sound experiments, implement traffic splitting, collect metrics, and perform significance testing. Our platforms enable concurrent testing of multiple variants, gradual rollout strategies, and automated winner selection. We track both technical metrics like accuracy and business outcomes like conversion rates, revenue impact, and user engagement. Comprehensive analysis tools help understand performance across segments and identify improvement opportunities. This data-driven approach ensures changes actually improve outcomes, quantifies business impact, reduces risk of deploying inferior models, and enables continuous optimization of AI systems.