Production ML & MLOps
Experimentation & Rollouts
A model that wins offline still needs controlled production validation. Shadow deployments, canaries, A/B tests, gradual rollouts, and rollback criteria turn model launches into safe experiments.
- Offline metrics are necessary, not sufficient
- Shadow deployment observes without acting
- Canaries limit blast radius
- A/B tests measure product impact
- Rollback criteria should be predeclared
- Guardrail metrics protect the system
| Stage | Answers |
|---|---|
| Offline eval | Does it beat baseline on historical examples? |
| Shadow | Can it run on real traffic without acting? |
| Canary | Does a tiny real slice look safe? |
| A/B test | Does it improve product outcomes? |
| Gradual ramp | Does quality hold at scale? |
| Full release | Can we monitor and roll back continuously? |