Modern AI applications need more than strong models—they need reliable, low-latency, cloud-native systems capable of running at global scale. In this session, Samaresh shares engineering and architectural lessons from building Hydra a next-generation distributed AI/ML orchestration platform that runs across hybrid cloud and edge environments.

The talk breaks down practical patterns for designing robust AI microservices:

1. event-driven and message-driven architectures,

2. containerized ML pipelines,

3. GPU/CPU/NPU workload scheduling,

4. real-time telemetry and observability,

5. concurrency, resilience, and back-pressure strategies,

6. achieving low-latency inference in unreliable network conditions.

Attendees will also learn how AI workloads benefit from cloud-native principles—Kubernetes, service meshes, asynchronous messaging, distributed caching, secure communication, and zero-downtime deployments.