Data Labeling

Data
About 1 min read

The process of assigning correct answers (labels) to data for supervised machine learning, serving as the foundation of model quality.

Also known as
Data LabelingData LabelingAnnotation

Detailed explanation

Data labeling is the process of attaching correct answers (labels) to data for use in supervised machine learning. Typical tasks include drawing bounding boxes around objects in images, classifying sentiment or categories in text, or transcribing speech into text. Because model performance heavily depends on the quality of training data, the accuracy of labels and the consistency of standards determine model accuracy. Methods such as crowdsourcing, in-house expert labeling, and semi-automated approaches (where a model suggests initial labels and humans review them) are used. Specialized platforms like Scale AI and Labelbox support workflow management and quality verification.

Why it matters in tool selection

The quality control features of a labeling tool directly translate to model accuracy. The core issues are whether multiple annotators label with consistent standards, whether review and consensus processes exist, and whether progress and accuracy can be tracked. If data security is critical, you should also consider whether labeling can be done in-house rather than using external crowdsourcing.

What to look for when choosing a tool

  • Does it have quality control features, such as annotator consensus and review?
  • Does it support the required data formats (images, text, audio)?
  • Can it accelerate the process with semi-automated features where a model proposes initial labels?
  • Does it support in-house labeling and access controls for sensitive data?

Real-world examples

When building a dataset for autonomous driving, bounding boxes are drawn around vehicles, pedestrians, and signals across tens of thousands of road images. If standards differ among annotators, the model becomes confused; therefore, a labeling tool that provides consensus reviews and guidelines is used to ensure label consistency.

Related terms

Machine LearningDeep LearningSynthetic Data