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As PyTorch models move to production, organizations face a critical challenge: deploying, monitoring, and operating inference at scale across multiple regions. Single-region serving is well-understood, but multi-region LLMOps—model distribution, observability, failover, and cost management—remains ad-hoc and challenging for multiple customers.
This session presents production-tested architectures for multi-region PyTorch inference and LLMOps workflows. We cover:
Serving: Multi-region TorchServe/KServe on Kubernetes with latency-based routing, blue-green deployments, model versioning, and automated failover with circuit breakers.
Observability: OpenTelemetry distributed tracing, Prometheus/Grafana dashboards for latency, throughput, GPU utilization, and LLM-specific metrics like time-to-first-token and KV-cache hit rate.
LLMOps: CI/CD pipelines for cross-region model deployment with automated rollback, drift detection, and SLO-based alerting.
Attendees leave with serving architectures, dashboards, and deployment pipelines using open-source tooling.
Principal Engineer driving technical strategy and building mission-critical foundational platforms for AI, HPC, and distributed systems, bridging the gap between infrastructure, AI research, and product organizations.
