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If you are wondering what is data annotation, it refers to labeling or organizing raw data so AI and machine learning models can learn from it. Common examples include identifying objects in images, assigning categories to text, or converting spoken language into labeled transcripts. The exact process varies depending on the model being developed. For example, a healthcare image dataset may require specific regions to be marked for training an image-analysis system. Annotation provides structured examples that algorithms can learn from. Careful guidelines, consistent labeling, and quality reviews are generally needed to create reliable training datasets.