Machine Learning at the Edge

Edge computing moves workloads from centralized locations to remote locations and it can provide faster response from AI applications. Edge computing devices are getting deployed increasingly for monitoring and control of real world processes like people tracking, vehicle recognition, pollution monitoring etc. The data collected at the devices gets transported to centralized cloud servers over data pipelines and are used to train machine learning models. Training models needs lot of computational power and the current strategy is to train centrally and deploy on edge devices for inference. Already deep learning models are being used at the edge for critical problems like face recognition and surveillance.