Definition
Automated canary analysis routes a small percentage of production traffic to a new model or service version, continuously measures health and quality metrics, then automatically promotes or rolls back based on objective thresholds—removing humans from the critical decision loop.
HOW IT WORKS
Deploy canary alongside stable version. Direct 5-10% of traffic to canary. Run health checks every 30-60 seconds comparing metrics against baseline: success rate ≥99%, P99 latency <500ms, CPU <90%, business metrics (conversion rate not down >5%).
PROGRESSIVE ROLLOUT
If all checks pass, controller increases canary traffic by step amount (typically 5% increments). If thresholds violated repeatedly (5-10 consecutive failures), route all traffic back to stable within minutes and mark rollout failed. Full ramp from 0% to 50% takes 15-30 minutes with pauses between steps to accumulate signal.
💡 Insight: Small blast radius (only 5-10% exposed initially), fast automated detection (minutes not hours), and clear rollback paths without human intervention in critical loop.
ML SPECIFIC CONSIDERATIONS
Layer model quality signals on top of infrastructure SLOs. Beyond latency and error rates, monitor prediction quality: AUC drift, calibration error, CTR changes. Best accuracy requires several thousand requests per minute per instance—lower traffic produces noisy comparisons and false alarms.
⚠️ Key Metrics: Request success rate, P99 latency, CPU usage, plus ML-specific: prediction distribution shift, calibration slope, and business metrics like conversion rate or click-through rate.
✓Traffic starts at 5 to 10 percent canary, increases by 5 percent steps every 30 to 60 seconds if checks pass, typical ramp to 50 percent takes 15 to 30 minutes
✓Health checks combine infrastructure SLOs (99 percent success rate, 500 ms P99 latency, 90 percent CPU threshold) with ML or business metrics (CTR drop within 5 percent, AUC drift)
✓Automatic rollback triggers after 5 to 10 consecutive threshold violations, routes all traffic back to stable within minutes with no human intervention
✓Adobe reports best accuracy with several thousand requests per minute per instance, low traffic produces noisy comparisons and false rollback alarms
✓Systems compare canary metrics against baseline (stable version or dedicated instance set) in rolling windows of 3 to 5 intervals to smooth variance