Test what works
Automated testing, promotion and rollback
A new model goes live only when it beats the current one, and comes back out automatically if live performance drops.
What it does
The mechanism
- 01
Promotion is conditional: a candidate has to beat the incumbent on the metrics you agreed and stay inside your quality, diversity, latency and cost guardrails.
- 02
Releases run in shadow first, then as a canary against a small share of traffic, with automatic rollback if the live numbers move the wrong way.
What it gets you
Three things you can point at
01
Shadow mode
A candidate answers real traffic without serving it, so it can be compared against the incumbent on the same requests.
02
Canary and rollback
A small percentage of traffic goes first. Live performance is watched, and a regression rolls the release back without anyone being paged.
03
Guardrails, not just accuracy
Diversity, coverage, latency and cost are release conditions in their own right - a model that wins on relevance alone does not ship.