TinyML

TinyML refers to the deployment of machine learning algorithms on resource-constrained devices, such as microcontrollers and edge devices. It enables the execution of machine learning models with minimal power consumption, memory, and processing capabilities. The main characteristics of TinyML include low latency, energy efficiency, and the ability to function without a continuous internet connection. Common use cases for TinyML include smart home devices, wearables, and IoT applications, where real-time data processing and decision-making are crucial. By leveraging TinyML, developers can create intelligent systems that operate efficiently in diverse environments.