Compare Canary and Blue/Green ML deployments and model-specific metrics
WHAT IT TESTS: Model quality vs infra health in rollouts. ANSWER OUTLINE: Contrast Canary gradual shift vs Blue/Green instant swap; highlight silent failures, data drift, prediction distribution; cite accuracy and calibration.
WHAT IT TESTS: Whether you understand ML models fail silently and need quality signals beyond infra health. ANSWER OUTLINE: Contrast Canary's gradual traffic shift with Blue/Green's instant cutover; explain Canary exposes a small slice to catch data drift, prediction distribution shift, and accuracy regression; list model-specific metrics like per-class accuracy, calibration error, feature drift, and output divergence. RED FLAG: Treating the model as a black box and monitoring only latency, error rate, and CPU while ignoring prediction quality.
Read the original → oneuptime.com
- #mlops
- #canary
- #blue-green
- #model-serving
- #monitoring
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