Swim ESPTMis a streaming data analytics engine that takes advantage of a distributed network architecture to operate self-training Machine Learning (ML) at the edge in real-time. Swim ESP offers a simple approach to Machine Learning at the edge, which provides real-time learned insights for time-critical applications. Swim applies learning at the data source – such on industrial devices, Smart City sensors, Unmanned Aerial Vehicles (UAVs), or other autonomous vehicles. Swim ESP then shares learned insights with other virtual components using an efficient pub-sub protocol, automatically building a distributed, secure, & resilient fabric that spreads knowledge throughout the network.
We've put together an infographic comparing Machine Learning and Artificial Intelligence and highlight examples where edge computing solutions like Swim ESP can enable these applications in the future:
Learn how thehttp://www.swim.ai/swim-edx http://blog.swim.ai/2017/innovations-in-edge-computing-part2 streaming analytics engine enables Machine Learning applications at the edge.