A/B Testing Design for Machine Learning Evaluation

Deploying a machine learning model is only half the battle; the real challenge lies in proving that it actually drives business value. Designing rigorous A/B tests specifically for ML systems ensures you make data-driven decisions based on statistically sound results rather than guesswork. This written course guides you through the foundational principles of experimentation in ML environments. You will transition from understanding basic statistical concepts to designing robust, end-to-end A/B tests that accurately measure model performance in real-world scenarios. What you'll learn: Define clear hypotheses and select primary, secondary, and guardrail metrics for ML models; Calculate sample sizes and experiment runtimes to ensure statistical power and validity; Identify and mitigate common pitfalls like selection bias, sample ratio mismatch, and novelty effects; Contrast traditional A/B testing with modern MLOps evaluation patterns like shadow deployments and canary releases; Analyze experiment results using statistical significance testing and confidence intervals. The course begins with core terminology and statistical foundations before progressing to practical experimental design, metric selection, and post-test analysis. This course is designed for aspiring data scientists, machine learning enthusiasts, and product managers who want to understand how to evaluate models in production. No prior experimental design experience is required. Start reading today to master the art of scientific model evaluation.