Recipe: Shadow model deployment
Deploy a shadow model that mirrors your production inference pipeline for A/B evaluation, canary testing, and silent rollout validation.
1. Fork the inference endpoint
Clone your production model's serving configuration. Point the shadow to a separate weight snapshot or a candidate checkpoint you want to evaluate under live traffic.
2. Mirror traffic
Duplicate a percentage of production requests to the shadow endpoint. Discard shadow responses — the caller never sees them. Log both production and shadow outputs side-by-side for offline analysis.
3. Compare metrics
Track latency deltas, output distribution drift, and token-level agreement. Flag regressions automatically when the shadow diverges beyond your configured tolerance.
4. Promote or roll back
Once the shadow meets your stability and quality gates, swap it into production. If it fails, tear it down with zero user impact.
Tip: Run shadows on isolated compute to avoid noisy-neighbor interference with production workloads. Use Meridian's traffic-split primitives to control the mirror percentage without changing client code.