ab-test-framework-ml

What problem does it solve?

This Skill provides a comprehensive framework for designing, implementing, and analyzing A/B tests specifically for machine learning models, addressing the unique challenges of ML experimentation in production.

Core Features & Use Cases

• ML-Specific A/B Testing: Handles statistical rigor, sample size calculation, and traffic splitting for ML models.
• Model Deployment & Monitoring: Integrates with feature stores and monitors for concept drift and performance degradation.
• Statistical Analysis: Supports both frequentist and Bayesian analysis, including sequential testing.
• Use Case: A data science team can use this Skill to test a new recommendation engine model against the current one, ensuring statistically significant improvements in user engagement before full deployment.

Quick Start

Use the ab-test-framework-ml skill to calculate the required sample size for an A/B test with a baseline conversion rate of 0.15 and a minimum detectable effect of 0.03.