Simply put, Edge AI is a combination of Edge Computing and Artificial Intelligence. 

​

AI algorithms are processed locally, either directly on the device or on the server near the device. The algorithms utilize the data generated by the devices themselves. Devices can make independent decisions in a matter of milliseconds without having to connect to the Internet nor the cloud. Edge AI has almost no limits when it comes to potential use cases. Edge AI solutions and applications vary from smartwatches to production lines and from logistics to smart buildings and cities. 

​

How does Edge AI work, what type of business benefits does it bring and how can you get started with Edge AI? Read this page and find out - let's begin our journey to the edge!

​

👉 Edge Computing

Edge computing consists of multiple techniques that bring data collection, analysis, and processing to the edge of the network. This means that the computing power and data storage are located where the actual data collection happens. What is meant by the network edge? Well, depends on the use case - it could be a mobile phone, IoT-device, self-driving car or even a cell tower. 

 

👉 Artificial Intelligence

Broadly speaking, in Artificial Intelligence a machine mimics human reasoning: such as understanding languages and problem solving. Artificial intelligence can be seen as advanced analytics, (often based on machine learning) combined with automation. This pragmatic definition covers all current AI applications. 

​

You can think of Edge AI as analytics that takes place locally and utilizes advanced analytics methods (such as machine learning and artificial intelligence), edge computing techniques (such as machine vision, video analytics, and sensor fusion) and requires suitable hardware and electronics (which enable edge computing). In addition, location intelligence methods are often required to make Edge AI happen.

​

Edge AI devices include smart speakers, smart phones, laptops, robots, self-driven cars, drones, and surveillance cameras that use video analytics.

​

Although most of the analysis and decisions made by machines already happen on the edge, the greatest benefits are obtained when the findings produced by the devices are linked to business processes. Therefore, modern data platforms, capable of handling large amounts of location and streaming data, are also needed to enable real-time computing.

​

​