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AI & Machine Learning · Foundational

Data Labeling

The process of annotating raw data with labels so it can be used for supervised training or evaluation.

Definition

Labeling can be done by internal experts, crowdsourced workforces, vendors, or assisted by models through active learning and pre-labeling. Clear guidelines, inter-annotator agreement checks and audits are used to control quality. Sensitive data requires a private, vetted workforce.

Example

Drawing bounding boxes around pedestrians in dashcam images.

Exam tip

Active learning reduces labeling cost by sending only the most uncertain examples to humans.

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