Arc of AI Conference

Track · AI New and Noteworthy

Platform Engineering for Modern AI: From Multi-Cloud GPUs to Edge and Intelligent Tooling

Modern AI systems extend far beyond centralized training clusters. They now span multiple clouds, GPU-backed platforms, edge execution environments, and the tools developers use every day.

In this session, we’ll explore how platform engineering enables AI Ops and MLOps on Kubernetes while integrating edge inference and model orchestration through MCP servers. We’ll walk through a reference architecture that connects multi-cloud GPU clusters with edge workloads and intelligent services, showing how models move from training to deployment to real-time execution.

We’ll examine emerging patterns such as edge-first inference, workload-aware routing, and MCP-based coordination between models, services, and developer tools. We’ll also discuss how these patterns improve developer productivity by reducing operational friction, standardizing environments, and embedding intelligence directly into the software delivery lifecycle.

Attendees will leave with a practical understanding of how to design AI platforms that are scalable, portable, and optimized for both performance and developer experience.