CANARY DEPLOYMENT PHASES
Canary deployment for AI models follows five phases. Phase 1: shadow deployment with 100 percent traffic mirrored to canary without serving responses (duration: 1-24 hours, validates no crash). Phase 2: 1 percent traffic served by canary for latency and quality comparison (duration: 30-60 minutes, 500-1,000 requests). Phase 3: 5 percent traffic for robust quality validation (duration: 2-4 hours, 5,000-20,000 requests). Phase 4: 25 percent traffic for scale validation (duration: 4-8 hours). Phase 5: 100 percent rollout.
Total rollout duration: 8-36 hours depending on traffic volume and quality metric stability. Automated progression through phases with gating reduces manual oversight. Each phase validates: error rate below 0.5 percent, P99 latency within 20 percent of baseline, quality metrics within 3 percent of baseline, and GPU memory utilization within 10 percent of baseline.
| Phase | Traffic % | Min Duration | Min Requests | Validation Criteria | Rollback Time |
|---|---|---|---|---|---|
| Shadow (mirror) | 0% (mirror) | 1 hour | N/A | No crashes, no OOM | Instant |
| Trial | 1% | 30 min | 500 | Latency OK, quality OK | < 30 sec |
| Testing | 5% | 2 hours | 5,000 | All metrics within threshold | < 30 sec |
| Expansion | 25% | 4 hours | 25,000 | Scale validation passed | < 60 sec |
| Full rollout | 100% | N/A | N/A | Sustained stability | < 2 min |
QUALITY AND PERFORMANCE METRICS GATES
Canary validation requires automated metrics comparison between baseline and candidate. LLM quality metrics: perplexity change under 1 percent, ROUGE-L within 2 percent for summarization, BLEU within 2 percent for translation, embedding cosine similarity above 0.98. Performance metrics: P50/P95/P99 latency, throughput, TTFT, ITL, GPU memory utilization, and error rates.
Statistical testing: two-sample Kolmogorov-Smirnov test comparing latency distributions with p < 0.05 threshold. Sequential probability ratio test for quality metrics enables early stopping: gates can fail within 15 minutes for severe regressions versus 2+ hours for full sample. Minimum sample size: 500 requests per variant for latency metrics, 2,000 requests per variant for quality metrics at 80 percent statistical power.
MULTI-MODEL SERVING INFRASTRUCTURE
Multi-model serving infrastructure supports concurrent model versions during canary. Triton Inference Server with model version policy routes requests to specific model versions. Kubernetes deployments maintain both baseline and canary pods with separate resource pools. GPU allocation: baseline 60 percent, canary 30 percent, buffer 10 percent for traffic spikes. Model version isolation prevents canary resource contention from affecting baseline latency.
Infrastructure cost during canary: additional GPU capacity for canary model, doubling inference cost during rollout. For 1,000 QPS Llama 3 70B serving, baseline requires 8 H100 GPUs, canary requires 3 H100 GPUs (based on 30 percent traffic). Canary cost per deployment: $630-$1,050 in additional GPU cost for 8-36 hour rollout duration.
ROLLBACK AUTOMATION AND INCIDENT RESPONSE
Automated rollback triggers: error rate exceeding 1 percent over 1-minute window, P99 latency exceeding 2x baseline over 5-minute window, quality metric decline exceeding 5 percent over 15-minute window, GPU memory leak detected (monotonically increasing usage). Rollback completes in under 30 seconds via inference gateway configuration change to route 100 percent traffic to previous model version.
Post-rollback analysis: preserve canary deployment state (model weights, configuration, traffic logs) for root cause investigation. Automated diff generation between baseline and canary configurations identifies deployment configuration drift. Root cause categories for rollbacks: model quality regression 45 percent, latency degradation 25 percent, software configuration error 15 percent, resource exhaustion 10 percent, data drift 5 percent.