# Embedded Machine Learning (Edge AI Glossary)

## metadata
- term: Embedded Machine Learning
- slug: embedded-machine-learning
- letter: E
- route: /resources/glossary/embedded-machine-learning
- canonical: https://klyff.com/resources/glossary/embedded-machine-learning

## Definition & Context
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,...

## Details
## 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