midMLOps
What is A/B testing in ML model deployment?
Updated May 17, 2026
Short answer
A/B testing compares two model versions by splitting traffic and measuring performance.
Deep explanation
Traffic is split between control (old model) and variant (new model). Metrics like conversion rate or accuracy determine which model performs better. It ensures safe deployment of ML models.
Real-world example
Testing a new recommendation model on 10% of users before full rollout.
Common mistakes
- Not ensuring statistically significant sample size.
Follow-up questions
- What metrics are used in A/B testing?
- How long should A/B tests run?