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Edge AI


Edge AI refers to the integration and execution of artificial intelligence (AI) directly at the “edge” of a network, i.e. close to data sources, instead of sending this data to remote data centers or clouds for processing. This technology makes it possible to process data in real time where it is generated.


With the increasing adoption of the Internet of Things (IoT) and the need to process large amounts of data quickly, Edge AI has emerged. Originally, data was sent to central data centers or the cloud to be processed there. However, this approach can lead to latency issues and increased data transfer costs. Edge AI solves these problems by offloading processing to local devices, enabling faster and more efficient data processing.

Areas of application

Edge AI is used in various areas, including industrial automation, autonomous driving, health monitoring, and intelligent city planning. In industry, for example, production machines can be monitored and controlled in real time using Edge AI to increase efficiency and minimize downtime.


The key benefits of Edge AI include lower latency, increased data security as less data needs to be transferred, and improved efficiency through on-site processing. This technology enables companies to respond more quickly to changes and anomalies, resulting in higher productivity and lower costs.


The challenges of Edge AI include the need for powerful hardware on end devices and the complexity of implementing and maintaining such systems. Companies must also ensure that their Edge AI solutions are scalable and adaptable to meet ever-changing requirements.


A specific example of the use of Edge AI in industry is the use of a self-service portal for maintenance work on production machines. By integrating Edge AI, machine conditions can be monitored in real time and maintenance work can be automatically planned and carried out without the need for human intervention.


Edge AI enables real-time processing of data right at the source, reducing latency and increasing efficiency. This technology is widely used in various industries and offers significant benefits, but also poses challenges that must be overcome.