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ML Offloading APIs provide synchronization of ML inference service with UE side. It serves application developers and enable enables machine learning apps to offload computation intensive jobs from UE device to close by edge nodes. ML offloading services satisfy the requirement the ML computing resource requirement, meanwhile its responses faster than cloud ML services.
ML offloading APIs offer ML inference services (support different ML frameworks) from KubeEdge sites through ML APIs, which contains a set of commonly used model pool. Machine Learning models in the pool have detail features published and performance has been tested. It has different categories to cover a wide variety of use cases in ML domain. The ML API enables traditional app developers to leverage the fast response time of edge computing and lower entry barriers of machine learning knowledge. Just use those ML offloading API in app, and stable new ML features can be delivered to user devices from the nearest edge node. The KubeEdge ML offloading service has a Facial recognition demo api. Developer’s application can input face image to it via https request, and the edge ML offloading service identify identifies the expression and return corresponding facial code. It is a sample component of KubeEdge to address users' data security or latency concerns. With high scalability of model acceleration on demand. Mobile app developers don't need to worry about the device resource limitation and latency issues to the public cloud.
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