A/B Testing (in ML)
A controlled experiment comparing two model variants to determine which performs better on real users, essential for validating ML improvements in production.
How It Works
Traffic is split between the current model (control) and the new model (treatment). Key metrics are measured for both groups over a statistically significant period. If the treatment outperforms control with statistical significance, it's deployed.
ML-Specific Considerations
Unlike web A/B tests, ML experiments must account for model warm-up time, feedback loops (the model affects future data), and novelty effects. Proper randomization and guardrail metrics prevent shipping harmful changes.