Edge modeling, in the realm of computer science and artificial intelligence, refers to the process of deploying machine learning models on edge devices. Edge devices are any pieces of hardware that control data flow at the boundary between two networks. These include routers, routing switches, integrated access devices, multiplexers, and a variety of wide-area network (WAN) access devices. Mobile phones, IoT devices and appliances, and wearables can also be considered edge devices.
The main idea behind edge modeling, or ‘edge computing’, is to bring computation and data storage closer to the location where it’s needed, to improve response times and save bandwidth. Instead of sending raw sensor data to the cloud for processing, which can consume time and resources, edge devices make decisions locally using AI models. The deployment of edge models equips these devices with intelligence to make instantaneous decisions, even without connectivity and reduces reliance on the central server or the cloud.
Edge modeling is a significant shift in AI and machine learning as it enables faster responses, reduced storage costs, and better privacy. As IoT and smart devices become more widespread, and as the demand for real-time, reliable decision-making grows, the role of edge modeling is becoming increasingly important. It signifies a move towards decentralization in AI, where AI computations are performed right where the data is generated, providing quicker insights and more efficient use of resources.
« Back to Glossary Index
