Edge AI deployment notes for private inference

Start with the deployment boundary

Private inference succeeds when the boundary is clear. Decide what runs on-device, what can leave the device, what must be cached locally, and which failure modes should fall back to a simpler experience.

The first practical decisions

• Choose a model format and runtime that fit the target hardware instead of the demo machine.
• Set a latency budget before adding features, then measure every step against that budget.
• Keep evaluation examples close to the product workflow so model improvements map to user value.
• Document what data stays local and what data, if any, is allowed to cross a service boundary.

What makes it shippable

The product layer matters as much as the model. Users need feedback, recovery paths, and clear expectations when local inference is slow, uncertain, or unavailable.