# Labels (Edge AI Glossary)

## metadata
- term: Labels
- slug: labels
- letter: L
- route: /resources/glossary/labels
- canonical: https://klyff.com/resources/glossary/labels

## Definition & Context
Labels are descriptive tags or annotations assigned to data, enabling Edge AI models to understand, categorize, and make decisions from raw inputs. Also known as data tags, they...

## Details
## Labels

#### What is Labels?

Labels are descriptive tags or annotations assigned to data, enabling Edge AI models to understand, categorize, and make decisions from raw inputs. Also known as **data tags**, they are essential for supervised machine learning and accurate edge computing predictions.

#### Why Is It Used?

Labels guide AI algorithms to recognize patterns, classify objects, and improve decision-making directly on devices at the network edge, reducing latency and dependence on cloud processing.

#### How Is It Used?

 
- Assign labels to images, sensor readings, or video frames.
- Train Edge AI models to identify objects, detect anomalies, or trigger automated actions.
- Continuously refine models with updated labeled data for better real-time performance. 

#### Types of Labels

 
- **Manual Labels:** Human-generated annotations for high accuracy.
- **Automated Labels:** AI-assisted tagging for large datasets.
- **Hybrid Labels:** Combination of manual and automated labeling for efficiency and precision. 

#### Benefits of Labels

 
- Enhances accuracy of Edge AI predictions.
- Reduces cloud processing and network bandwidth.
- Enables real-time, on-device decision-making.
- Supports diverse use cases from IoT analytics to industrial automation.