...
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 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 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.
The ML offloading APIs is a set of intelligence services on edge cloud which offers various of AI services, and it can be triggered by mobile applications. For example, it can be used to determine if an image contains faces or translate text into different languages. Those APIs are available only if developers deploy it those through KubeEdge. The ML offlading offloading APIs can support different ML categories, including Vision, ASR, dialog engine and more in the future, ans serves as REST web service.
...
Facial Expression Recognition
This operations operation takes an input image and success response will be in JSON format with 6 of human facial expression alone with different scores.
...
Image type: PNG image
Image demensionsdimensions: greater than 48X48
Response
...