The purpose of this Document is to enumerate the APIs which are exposed by Akraino Blue print project to the external projects Akraino/Non Akraino for interaction/integration.

This document should be used in conjunction with the architecture document to understand APIs at  modular level and their interactions.

This document should function as a glossary of APIs with its functionality, interfaces, inputs and expected outcomes as the following example:

API1: Kubernettes native APIs 

API2: KubeEdge APIs (Kubernetes API extensions)

API3: ML inference framework APIs 

ML framework APIs offer ML inference services from KubeEdge sites through ML Engine, which is 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 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 from the nearest edge node. The ML engine contains vision, video, OCR, Facial recognition, and NLU sectors. Developer’s application can provide image or voice to the ML engine via https request, and the edge ML offloading service can identify objects, people, text, scenes and activities etc. This is a key component of MEC ecosystem, where user has 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.