Champion-Challenger Testing
Champion-challenger testing runs a candidate model (the "challenger") against the current production model (the "champion") on live traffic or a shared evaluation set, comparing their outcomes head-to-head to decide whether the challenger should replace the champion — a structured, ongoing version of an A/B test built specifically for model iteration.
The framing matters more than it sounds: naming one model the "champion" makes explicit that a new model has to actually beat the current one on defined metrics to earn deployment, rather than simply being deployed because it's newer or scored well on its own in isolation. This guards against a common failure mode — a new model that improves on one metric while quietly regressing another that wasn't part of the comparison.
A solid champion-challenger setup evaluates both models against the exact same inputs, whether live shadow traffic or a shared held-out set, using the same scoring method, and requires the challenger to win — or at minimum tie — on every metric that matters, not just the headline one. Teams that skip this and just compare summary accuracy numbers between runs often miss regressions in tail cases, latency, or cost that only show up when you look at the comparison in detail rather than at the average.
