# What is Data Labeling? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Model Training  
> **Canonical Citation:** [https://spidits.com/ai-glossary/data-labeling](https://spidits.com/ai-glossary/data-labeling)

## Definition
Data Labeling is the process of identifying raw data points (such as images, text, or audio files) and appending target category tags (labels) to them to create a labeled dataset for supervised learning.

## Architectural Mechanics & Deep Dive
Data labeling is the process of identifying raw data (such as images, text, or audio files) and adding informative tags or annotations to provide context for machine learning models. Labeling is the foundation of supervised learning, where models learn to recognize patterns based on these ground-truth labels. Because manual labeling is expensive and time-consuming, companies use automated data labeling tools, crowd-sourced annotation services, or self-supervised models to scale dataset creation.

## Industry Applications & Real-World Use Cases
Supervised training dataset preparation, human annotation setups, and labeling quality audits.

## Frequently Asked Questions

### What is semi-supervised learning in relation to data labeling?
A training approach that combines a small amount of labeled data with a large amount of unlabeled data, allowing the model to propagate labels automatically to reduce labeling costs.

### What are popular tools for automating data labeling?
Programmatic labeling tools like Snorkel, using weak supervision rules, or employing LLMs to draft initial label predictions for human review.

## Related Terminology
- [label](https://spidits.com/ai-glossary/label)
- [supervised-learning](https://spidits.com/ai-glossary/supervised-learning)
- [dataset-curation](https://spidits.com/ai-glossary/dataset-curation)

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*From the SPIDITS AI Knowledge Base (336+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*