Architecture is the set of responsibilities and constraints behind the visible request path. These guides connect content distribution, inference placement, and resource accounting so an individual optimization does not hide a problem elsewhere.
Start with the input, the useful result, and the systems between them. Mark trust boundaries, ownership, and dependencies. A diagram is helpful when each box names an actual responsibility and every connection has a reason to exist. Revisit the design under limited connectivity and reduced capacity.
Connect these ideas with the Edge AI overview. If the question spans several parts of the system, the guide directory offers a sequence through the related decisions.
A useful AI budget follows each request from the edge to the model and back. Learn to separate billing from operational cost, model retries and caching, and connect infrastructure spending to successful user outcomes.
Inference placement starts with the job a model must do. Learn how to compare local devices, nearby servers, and cloud services using complete latency budgets, realistic memory measurements, privacy boundaries, and explicit fallback rules.
Machine learning can help a delivery system choose what to cache, where to send traffic, and when to investigate unusual behavior. Start with a narrow decision, a clear baseline, and boundaries that remain dependable when predictions fail.