Machine Learning and Edge
The basic concept behind edge computing is the idea of distributing computing intelligence across an entire network instead of centralizing it in the cloud. Edge computing is advantageous to machine learning because edge computing relies on proximity to the source of the data, and it minimizes incurred latency. Applications that combine edge computing and machine learning enable new kinds of experiences and new kinds of opportunities in industries ranging from Mobile and Connected Home to Security, Surveillance, and Automotive Industry.
It dramatically reduces the amount of data that has to be sent over the network, thereby reducing network congestion, speeding up operation and, in many instances, reducing costs. In addition, the performance is typically much better because the processing can be done in real-time , and avoid any potential latencies or other delays that can occur across any type of network connection. In the case of surveillance, real-time analysis can make the difference between safety and disaster!
Products that combine these two technologies can save time, improve privacy, reduce network traffic, and enable applications or devices to be optimized for specific environments. Examples of ML over the Edge are autonomous cars and smartphones with facial/ iris recognition. Predictive maintenance in industry can be had by leveraging machine learning algorithms on edge computing hardware that is physically near these critical machines.
Machine learning can turn into a strong analytical tool for huge volumes of data. The blend of machine learning and edge computing can channel a large portion of the data gathered by IoT gadgets and leave the significant information to be analyzed by the edge and cloud analytic engines.
The issue of latency is what is driving numerous organizations to move from the cloud to the edge today.