Running AI Inference on Edge Hardware for Robotics

This article covers production engineering patterns, architectural tradeoffs, and lessons from real world system design.

The content looks at both the underlying concepts and the practical details that matter when you are running systems under real load.

Drawing from hands on experience across distributed systems, cloud native platforms, and enterprise architecture, the goal is to give you something more useful than a tutorial.

Architecture decisions are always context dependent. The aim here is not to prescribe one answer but to give you the mental models and tradeoff frameworks that help you reason clearly about your own systems.

Good architectures come from clear problem statements, honest constraint analysis, and iteration over time rather than from copying patterns from companies whose scale and context are completely different from yours.