A dependable deployment delivers a compatible set of artifacts and preserves an authorized recovery path. This collection connects immutable model releases to the security boundaries that protect manifests, assets, and runtime configuration.
List every artifact needed for a usable model, including auxiliary data and preprocessing. Test those assets together under production-style cache and browser policies. Preserve the prior approved release, and describe exactly how clients discover a rollback when release pointers may be cached.
Connect these ideas with the Model delivery overview. If the question spans several parts of the system, the guide directory offers a sequence through the related decisions.
Edge AI security spans downloaded artifacts, inference requests, retrieved context, and the tools a model can reach. Use this practical checklist to make each trust boundary explicit and verify that release and runtime controls work together.
Reliable model delivery requires more than uploading an ONNX file. Build a release process that binds models to runtime assets, verifies downloaded bytes, handles browser compatibility, and activates updates only when a complete working package is ready.