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API3: ML inference framework APIs 

API4: ML inference offloading APIs 

ML Offloading APIs provides synchronization of ML inference service with UE side. It serves application developers and enable machine learning apps to offload computation intensive jobs from UE device to close by edge nodes. ML offloading services satisfie the requirement the ML computing resource requirement, meanwhile its responses faster than cloud ML services. 

ML offlading ML framework APIs offer ML inference services (support different ML frameworks) from KubeEdge sites through ML EngineAPIs, which is contains a set of commonly used model pool with standard APIs. 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 engine enable 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 device devices from the nearest edge node. The ML engine contains vision, video, OCR, Facial recognition, and NLU sectors. Developer’s application applications can provide inputs ( image or voice) to the ML engine offloading APIs via https request, and the edge ML offloading service can identify objects, people, text, scenes and activities etc. This It is a key component of MEC ecosystem, where user has KubeEdge to address users' data security or latency concerns, therefor, can’t use cloud resource.  With high scalability of model acceleration on demand, mobile app developer no need to worry of on device resource limitation, and latency to the public cloud.

API4: ML inference offloading APIs 

ML Offloading APIs provides synchronization of ML inference service with UE side. It serves application developers and enable machine learning apps to offload computation intensive jobs from UE device to close by edge nodes. ML offloading services satisfie the requirement the ML computing resource requirement, meanwhile its responses faster than cloud ML services.