Edge AI deployment means running artificial intelligence models directly on devices like smartphones, cameras or sensors, instead of sending data to remote servers for processing. This approach allows decisions to be made quickly on the device, which can be important for tasks that need fast response times or for situations where there is limited internet…
Edge computing integration is the process of connecting and coordinating local computing devices or sensors with central systems so that data can be processed closer to where it is created. This reduces the need to send large amounts of information over long distances, making systems faster and more efficient. It is often used in scenarios…
TinyML frameworks are specialised software tools that help developers run machine learning models on very small and low-power devices, like sensors or microcontrollers. These frameworks are designed to use minimal memory and processing power, making them suitable for devices that cannot handle large or complex software. They enable features such as speech recognition, image detection,…
Edge AI optimisation refers to improving artificial intelligence models so they can run efficiently on devices like smartphones, cameras, or sensors, which are located close to where data is collected. This process involves making AI models smaller, faster, and less demanding on battery or hardware, without sacrificing too much accuracy. The goal is to allow…