Testing on reality with zero risk
In a shadow deployment the new model receives a copy of live requests and produces predictions that are logged but never returned to users. The current model still serves everyone. You get real world behavior without any user impact.
What shadowing reveals
- Operational fit, real latency, memory, and failure rates under production load.
- Output comparison, how often the new model disagrees with the live one.
- Distribution check, whether predictions look sane on real inputs.
Shadow versus canary
- Shadow never affects users, ideal for first contact with production traffic.
- Canary does serve a slice, so it tests true user outcomes the shadow cannot.
A common path is shadow first to validate stability, then canary to measure real impact.
Limits
Shadowing cannot measure effects that depend on the user seeing the prediction, such as click behavior, since the output is hidden.
Key idea
Shadow deployment feeds live traffic to a new model whose outputs are logged but never served, validating stability and outputs with zero user risk before a canary.