Canary model validation
The safest place to find a model regression is a small, representative cohort. Meshanics combines signed model releases, canary waves, health or model-evaluation probes, halt rules and manual approval so a candidate must prove itself before a wider rollout.
The principle
A candidate model and its runtime should be evaluated on the same hardware profile used in production. The signed release identifies the compatible profile, and a rollout can target an explicit lab or canary group before it widens.
Validation controls
The deployment loop limits how far an unhealthy candidate can travel:
- A canary wave exposes the candidate to a small cohort first.
- A health probe runs after the swap and triggers automatic rollback on failure.
- A halt rule pauses the rollout fleet-wide if failures cross your threshold.
- The previous model is retained on-device for instant local rollback.
For quality checks, use a signed model eval probe so the candidate must meet the
declared threshold before it is committed. Model Quality telemetry can then compare
bounded aggregate confidence, latency and error statistics across versions and
hardware profiles. Raw inputs and outputs do not leave the device.
Meshanics does not run a second model beside production or feed it live workload inputs. If your application performs shadow inference, report only the bounded aggregate results through Model Quality telemetry and keep promotion behind a manual rollout gate.