Artificial Intelligence (AI) and Machine Learning (ML) continue to disrupt legacy business processes with expansive integration capabilities. In a bid to match enterprise processor power demands, providers of machine learning development services are extending support to hardware devices. Edge machine learning is emerging as an effective substitute for cloud-based infrastructures to build and deploy enterprise ML models. From image recognition to predictive analytics, let’s explore how your business can experience automation in a cost-effective yet efficient way with edge machine learning mechanisms.

Understanding Edge Machine Learning

Edge machine learning (ML) refers to the practice of processing ML algorithms and data over local devices. It is contrary to the enterprise practice of training ML models in the cloud and uploading algorithms into the device. Under this mechanism, everything from data flow to the development and training of ML models is done on the device itself.

Several intelligent hardware products supporting edge machine learning include YOLO, MobileNets, Solid-state Drive (SSD), and Azure FPGA (Field-programmable Gate Arrays). Among these, FPGAs are emerging as more flexible and efficient mechanisms to implement ML logic.

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