What is Data Labeling? - Definition & Meaning

Definition

Data labeling is the process of adding labels, tags, or annotations to raw data that an ML model needs to learn. It is a crucial step in supervised learning and directly determines model quality.

Technical explanation

Labeling includes: classification (one label per item), object detection (bounding boxes), segmentation (pixel-level), NER (entity spans in text), sentiment, and more. Methods: manual (experts or crowd), semi-automatic (model suggests, human confirms), active learning (model selects which samples to label). Tools: Label Studio, Labelbox, Scale AI, Amazon SageMaker Ground Truth. Quality: inter-rater agreement, consensus, review loops. Cost-quality trade-off matters.

How AVARC Solutions applies this

AVARC Solutions supports clients with labeling strategies, tool selection, and quality control. We use active learning to minimize labeling effort and build custom annotation interfaces for domain-specific tasks. Where needed we integrate synthetic data to reduce labeling.

Practical examples

• An image classification model trained on thousands of labeled product photos; labels are product categories.
• An NER model for contracts where legal experts annotate entities (parties, dates, amounts) for training.
• An active learning pipeline selecting the 500 most uncertain samples for human labeling, saving 80% of effort.

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