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ML offlading 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 user cases in ML domain. The ML API enables traditional app developer 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 feature 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 the expression and return coorsponding 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 developerd don't need to worry of on device resource limitation, and latency issues to the public cloud.
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