Embedded Machine Learning
What is Embedded Machine Learning?
Embedded Machine Learning (EML) brings AI models directly onto edge devices, enabling real-time data processing without relying on cloud connectivity. Also known as on-device AI, it allows machines to learn and make decisions locally, enhancing speed and efficiency in Edge AI systems.
Why Is It Used?
EML is used to reduce latency, improve privacy, and ensure reliable AI-driven insights in environments with limited or intermittent connectivity, making Edge AI applications faster and more autonomous.
How Is It Used?
It is deployed on IoT devices, sensors, and industrial equipment to perform tasks like predictive maintenance, anomaly detection, and real-time image or speech recognition directly on the device.
Types of Embedded Machine Learning
TinyML: Lightweight ML models optimized for microcontrollers.
On-Device AI: AI models running directly on smartphones, cameras, or sensors.
Federated Learning: Collaborative training across devices without sharing raw data.
Benefits of Embedded Machine Learning
Low latency decision-making
Reduced cloud dependency and bandwidth usage
Enhanced data privacy and security
Real-time insights for mission-critical applications