Data annotation is the process of labeling raw data — text, images, audio, or video — with tags or metadata so that machine learning models can learn to interpret it correctly.
Common annotation tasks include tagging objects in images (bounding boxes), classifying text sentiment, transcribing audio, or marking named entities in documents.
Supervised machine learning models rely on annotated examples to learn the relationship between an input and the correct output. Higher-quality, consistent annotation generally leads to more accurate models.
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