Machine learning on edge devices

Smart IOT edge devices must take real-time actions based on the data collected and analyzed. Currently, most of the machine learning inferences are done on the server side and the actions are passed down to the IOT devices. Because all the data has to be transferred back to the server for analytics and inference, it leads to the high total cost of ownership(TCO). Components adding to TCO includes bandwidth utilization, storage requirements, and server-side computation. Also, since the inference is passed down from the server, it leads to non-real time actions on the edge devices. I will be discussing the new architecture that we built for executing machine learning and analytics on edge devices using Splunk's late binding schema techniques.