Recommended infrastructure and deployment flow optimized for reliability, scale, and operational clarity.
Containerize the object detection model with explicit GPU support and VRAM footprint budgeting. Use Nvidia-Docker, set resource limits if you request 32GB VRAM on a 24GB GPU, you’ll get repeated OOM killed containers.
Push image to registry. Run deployment through CI/CD; enforce gate checks on image size (<6GB container image is an ops threshold here, or rollouts get slow).
Agents are deployed to pre-warmed GPU pools in geographically matched regions (i.e., Mumbai for India-heavy user base), with ‘hot swap’ ready instances avoiding cold start.
Attach agent scaling to queue depth and API QPS, not just CPU/GPU use. When QPS jumps 10x, aim to scale from 2 to 20 agents in <90s. But, be aware: network bandwidth to storage can choke here monitor >80% network saturation and alert if batch drift increases beyond preset threshold.
Integrate metrics with Prometheus; specifically, alert if VRAM usage over 70% for 5 minutes, 95th percentile request latency >250ms, or failed batch rate >2%. Set alerts to notify in <60s; no point in a passive dashboard.
Implement disaster recovery with cold standby agents in a fallback region. Recovery runbook should target <5min full agent redeploy after catastrophic GPU node failure. For missed health pings or consistent 5xx, auto-drain traffic and rollback within 2 minutes. We’ve seen failures extend beyond 10min when no out-of-band alerting.
During model swap or upgrade, use a blue/green strategy with shadow traffic to the new agent version. Ramp up over 5-10% batches before shifting all production. Keep ability to instantly revert by toggling route if inference errors spike.
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