Data labeling is the process of assigning meaningful tags or classes to raw data so a machine learning model can learn from it. It is the hands-on step where humans, or models, mark what each example contains, and it is often used interchangeably with data annotation.
Data labeling is the practical act of attaching labels to raw examples so they can train or evaluate a model. Depending on the task, a label might be a class tag on an image, a transcription of a clip, or a box around an object, but the defining idea is the same: a human or a model decides what each piece of data represents and records it. Labeled data is what supervised learning runs on, the model learns the mapping from input to label.
Key takeaways
Data labeling assigns the tags or classes that turn raw data into supervised training examples.
It is largely synonymous with data annotation, labeling leans toward the act of tagging, annotation toward the broader practice including geometric labels.
Labeling is usually the most time-consuming and expensive stage of the ML pipeline.
Who does the labeling
In-house teams: subject-matter experts or dedicated labelers, highest control.
Outsourced vendors and crowds: scale at lower cost, with more quality-management overhead.
Model-assisted and automated labeling: a model pre-labels and humans verify, now the dominant pattern.
How it works, and how FiftyOne fits
Define a schema and guidelines, label against them, review for quality (often multiple labelers plus a consensus step), then export to a training format. Throughput, cost per label, and quality are the three levers operations teams balance. FiftyOne wraps that loop: you curate the data actually worth labeling, route it to your existing annotation tool, and pull the finished labels back to inspect quality before they reach training.
Data labeling vs data annotation
How data labeling and data annotation differ in emphasis, though the terms are used interchangeably.
How data labeling and data annotation differ in emphasis, though the terms are used interchangeably.
Data Labeling
Data annotation
Emphasis
The act of assigning tags or classes
The broader practice, including geometric labels
Typical label types
Class tags, transcripts
Boxes, masks, keypoints, relationships
Typical use
Classification and many NLP tasks
Computer vision and multimodal AI
In practice the terms are largely interchangeable and the difference is emphasis, not a hard line.
Why it matters
Labeling is the bottleneck and the cost center of most ML projects, so how you run it decides how fast and how affordably you can ship. The expensive variable is not the per-label price, it is rework. A label sent back for correction can cost several times the original, so quality at the source, clear guidelines and agreement checks, beats cheap, fast labeling you then pay to redo. Teams that optimize for price-per-label without measuring rework usually pay more in the end.
Frequently asked questions
What is the difference between data labeling and data annotation?
They are largely synonymous. Labeling leans toward assigning tags, annotation toward the broader practice including geometric labels.
Who does data labeling?
In-house teams, outsourced vendors or crowds, or models with human review.
Why is data labeling so expensive?
It is labor-intensive and scales with the data, and rework from quality issues multiplies the true cost.