Machine Learning at Edge helps to act on predictions near real-time. The predictive maintenance helps businesses to avoid catastrophes. Trained Machine Learning model running on Edge can help to act quickly based on the predictions where time critical action is required. Overall cost can be reduced by 20% by moving Machine Learning to Edge.

In a few years, the world will be filled with billions of small, connected, intelligent devices. Many of these devices will be embedded in our homes, our cities, our vehicles, and our factories. Some of these devices will be carried in our pockets or worn on our bodies. The proliferation of small computing devices will disrupt every industrial sector and play a key role in the next evolution of personal computing.

Most of these devices will be small and mobile. Many of them will have limited memories (as small as 32 KB) and weak processors (as low as 20 MIPS). Almost all of them will use a variety of sensors to monitor their surroundings and interact with their users. Most importantly, many of these devices will rely on machine-learned models to interpret the signals from their sensors, to make accurate inferences and predictions about their environment, and, ultimately, to make intelligent decisions. Offloading this intelligence to the cloud is often impractical, due to latency, bandwidth, privacy, reliability, and connectivity issues. Therefore, we need to execute a significant portion of the intelligent pipeline on the edge devices themselves.

Paasmer Machine Learning is another key feature in Paasmer-Docker which provides the user with a generic Machine Learning framework that allows the user to perform Machine Learning with his own dataset and train & test the Machine Learning module using few sets of commonly used Machine Learning algorithms.