How would you implement shadow deployment and which metrics justify promotion?
Tests zero-impact validation when feedback loops are broken. Mirror traffic to a shadow variant, log predictions, and compare latency, errors, and drift against SLAs. Red flag: calling it A/B testing or claiming live business metrics from unserved responses.
Tests safe model promotion when predictions lack closed-loop business feedback. A correct design mirrors traffic to a shadow variant, returns only the production response, and logs shadow outputs for offline analysis. Monitor operational regressions like p99 latency, error rate, and resource saturation, plus model-quality signals like prediction drift and ground-truth accuracy once labels arrive. Promotion requires a bake period with no SLA breaches. Red flag: calling it A/B testing or claiming revenue metrics from unserved predictions.
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- #mlops
- #shadow-deployment
- #model-serving
- #sagemaker
- #production-variants
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