Deploy UltraEdge HPC-I for AI and mixed workloads on Arm

About this Learning Path

Who is this for?

This is an advanced topic for business, R&D, and engineering teams seeking to optimize CPU and GPU infrastructure utilization while reducing total cost of ownership on edge and constrained environments. It's ideal for innovation and development teams building next-generation AI workloads using alternative runtime environments and packaging technologies.

What will you learn?

Upon completion of this Learning Path, you will be able to:

• Understand the layered architecture of UltraEdge core, boost, and prime
• Build applications using the UltraEdge MicroStack
• Deploy the MicroPacs on Linux-based compute systems and scale to cloud or data-center environments
• Optimize performance for edge-cloud scenarios, enabling near real-time data flows

Prerequisites

Before starting, you will need the following:

• Experience using Linux on embedded or SBC platforms
• Understanding of container runtimes (containerd) and CNI networking
• Basic knowledge of communication protocols (MQTT, HTTP, and others)
• Familiarity with edge-cloud architectures and data-flow orchestration