Edge AI is the practice of deploying artificial intelligence (AI) algorithms and models on edge devices such as smartphones, IoT sensors, and other devices with limited computing power, instead of relying on cloud servers. The purpose of edge AI is to process and analyze data in real-time, closer to the source, which results in increased speed, enhanced security, and decreased costs.

Edge AI can enable many practical use cases, such as predictive maintenance, anomaly detection, and autonomous driving. For example, in predictive maintenance, edge AI can analyze data from sensors in manufacturing equipment or vehicles and detect potential issues before they become major problems, allowing for preventative maintenance to be scheduled. In autonomous driving, edge AI can process data from cameras and sensors in real-time to make split-second decisions about steering, acceleration, and braking.

By leveraging edge AI, organizations can achieve faster, more efficient, and cost-effective processing of data. It also allows for more privacy and security, as all data does not need to be transferred to cloud servers for processing. As edge devices continue to proliferate and become more powerful, edge AI is expected to become increasingly important in enabling intelligent decision-making.