Definition

Data labeling means adding tags or descriptions to raw data—like marking photos with what's in them or classifying emails as spam. This labeled data helps AI models learn how to recognize, sort, or understand things.

Example

“Labeling images of cats and dogs helps AI learn to tell the difference between them.”

How It’s Used in AI

Labeled data is used to train models in image recognition, speech analysis, sentiment detection, and more. Without labeled examples, many types of AI—especially supervised learning—wouldn’t work well. It’s a key step in building accurate models.

Brief History

Data labeling has been around since early machine learning. It became more critical in the deep learning era when large, high-quality labeled datasets were needed to train powerful models like ImageNet and GPT.

Key Tools or Models

Labeling platforms include Labelbox, Scale AI, Amazon SageMaker Ground Truth, and Snorkel. Labeling can be manual (done by people) or automated (done by AI trained on earlier labeled data).

Pro Tip

More data isn't always better—good labels matter more. Clean, consistent labeling makes AI smarter and more reliable.