Exploring ML on the edge

Moving ML to an edge device has several advantages and use cases, depending on the specific requirements of a project or application. Here are some reasons why you might consider deploying ML to edge devices:

• Low latency and real-time processing: Edge devices are located close to the data source or the point of action, which reduces the time it takes for data to travel to a centralized server or cloud. This proximity allows for real-time processing, making it suitable for applications where low latency is critical, such as autonomous vehicles, industrial automation, and robotics.
• Privacy and data security: Edge computing allows sensitive data to be processed locally on the device, rather than sending it to a remote server or cloud. This can enhance privacy and data security by reducing the risk of data breaches during transit and storage. It’s especially important in applications such as healthcare, finance, and surveillance.
• Bandwidth...