A modern farm has hundreds of sensors to monitor the soil health, and robotic machinery to reap the vegetables. A modern shipping yard has hundreds of computers working together to orchestrate and analyze the freight that is coming in from overseas. A modern factory has temperature gauges and smart security cameras to ensure workplace safety.
All of these devices could be considered “edge” devices.
Over the last decade, these edge devices have mostly been used to gather data and save it to an on-premise server, or to the cloud. Today, as the required volumes of data and compute scale, we look for ways to better utilize our resources. We can start to deploy more application logic to these edge devices, and build a more sophisticated relationship between our powerful cloud servers and the less powerful edge devices.
The soil sensors at the farm are recording long time series of chemical levels. The pressure sensors in a centrifuge are recording months and years of data. The cameras are recording terabytes of video. These huge data sets are correlated with labeled events–such as crop yields.
With these large volumes of data, we can construct models for responding to future events. Deep learning can be used to improve systems over time. The models can be trained in the cloud, and deployed to devices at the edge.
Aran Khanna is an AI engineer with Amazon Web Services, and he joins the show to discuss workloads at the cloud and at the edge–how work can be distributed between the two places, and the tools that can be used to build edge deep learning systems more easily.
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