Description: The deployment of machine learning in the context of edge computing refers to the process of integrating a machine learning model into a production environment that operates on peripheral devices, such as sensors, cameras, and other IoT (Internet of Things) devices. This approach allows machine learning models to perform inferences and process data locally, rather than relying on cloud servers. This not only reduces latency but also minimizes bandwidth usage and enhances data privacy, as sensitive information can be processed without needing to be sent to the cloud. Key features of edge deployment include optimizing models to be lightweight and efficient, the ability to operate in real-time, and adapting to environments with limited resources. The relevance of this practice has grown in recent years due to the increase in connected devices and the need for fast and efficient solutions in applications such as surveillance, healthcare, industrial automation, and autonomous systems. In summary, the deployment of machine learning in edge computing represents a significant evolution in how artificial intelligence models are utilized, allowing for greater autonomy and efficiency in data processing.
