A/B Testing

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A/B testing is a controlled experiment used to compare two versions of a model or system in real conditions. In machine learning, traffic is split so that some users or requests see version A (usually the current model) while others see version B. Both versions run at the same time, and teams watch how each one performs on meaningful metrics. This helps confirm whether a new model truly performs better in the real world.

Running a good A/B test requires careful setup. The traffic split needs to be random, the test must run long enough to gather reliable data, and the evaluation has to be statistically sound to avoid false conclusions. Once results are clear, the stronger model can be rolled out more widely. A/B testing is one of the safest ways to improve production models, because each change is validated with real users before becoming the new default.

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