Do not start with device or cloud
Classify an edge AI placement decision by uncertainty before choosing device, gateway, cloud, or hybrid inference.
The move: classify uncertainty before choosing architecture. Cynefin separates work by how clear cause and effect are. Edge AI rarely starts as a pure engineering choice. A model can be accurate in notebooks and still fail at the edge because lighting, heat, connectivity, privacy, maintenance, or human workflow changes the problem. Clear: copy the proven pattern If the environment is stable and the constraints are routine, reuse a known pattern. Example: a barcode reader with fixed lighting and a vendor-supported model. Complicated: compare with experts If trade-offs are knowable but not obvious, bring in the right experts. Example: choosing between…
Sign up free — one personalized lesson every day, matched to your role and goals.
Already have an account? Sign in