Session
Building Real-Time Edge AI Systems: From Cloud-Native to On-Device Intelligence
Modern applications increasingly demand real-time intelligence, low latency, and privacy-aware analytics—capabilities that centralized cloud systems alone cannot deliver. This session walks through how to design and build production-grade Edge AI systems using cloud-native principles, container orchestration, and lightweight ML runtimes running on CPUs/GPUs/NPUs.
Drawing from hands-on experience building HP Hydra, HP AI Studio, and HP Z Boost, the talk covers:
How cloud–edge architectures differ from traditional cloud apps
Deploying AI/ML workloads across heterogeneous hardware
Real-time inference, data locality, and intelligent task routing
Observability, CI/CD, and DevOps considerations for edge fleets
Security foundations: PKI, TLS/mTLS, attestation, and zero-trust edge
Lessons learned scaling AI at the edge for enterprise customers
This session is designed to be useful to developers, architects, students, and anyone curious about the future of distributed AI.
Samaresh Kumar Singh
Principal Engineer, HP – Distributed Systems, Edge AI/ML, Cloud-Native Orchestration and Security
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