Shadow Deployment
A deployment strategy where a new AI model receives live production traffic in parallel with the existing model but does not serve predictions to end users, used for validation and comparison.
In plain language
Running a new AI model alongside the current one, processing the same requests, but not actually using its results. This lets you compare performance risk-free before switching over.
Why this matters
Shadow deployment is relevant to implementation because it provides a lower-risk validation pathway before production switchover. Your deployment governance should define when shadow deployment is required, what metrics are used to validate readiness and who approves the transition to live serving.
Relevance
ImplementationShadow deployment is a key mitigation for deployment risk, requiring documented performance criteria and approval gates before transitioning new models to production serving.
Related terms
Putting shadow deployment into practice in your organisation?
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