What is Data Labeling?
Data labeling is the process of adding tags or annotations to raw data so that machine learning models can learn from it during training.
It turns unlabeled examples into training examples by attaching meaningful information, such as class names, bounding boxes, or text categories. This step is required for most supervised learning tasks.
Labeling can be performed by humans, automated tools, or a combination of both. Quality and consistency of labels directly affect how well a model will perform.
Common formats include image classification tags, object detection boxes, sentiment scores on text, or transcribed speech segments.
Example
A person looks at thousands of photos and clicks 'cat' or 'dog' on each one so an image classifier can later recognize new pictures correctly.
Why it matters
Most high-performing AI systems today are trained on large amounts of labeled data; without accurate labels, models cannot learn reliable patterns.
Frequently asked questions
It is often done by human annotators, sometimes assisted by software that suggests labels for review.
Related terms
Supervised learning is a machine learning method where a model is trained on data that already has correct answers attached, so it can learn to predict those answers for new data.
Training data is the dataset of examples that a machine learning model learns from during the training process. It contains input features paired with known outputs so the model can discover patterns.
Active learning is a machine learning technique where the model itself selects the most informative unlabeled data points to be labeled by a human, rather than labeling data randomly or all at once.
A dataset is a structured collection of data points used to train, validate, or test machine learning models.
Batch size is the number of training examples processed together in a single forward and backward pass during model training.
Chunking is the process of breaking large datasets, documents, or files into smaller, fixed-size or semantically meaningful segments. It is a common data preprocessing step in AI/ML pipelines to manage memory and enable efficient processing.