Data Annotation

Data annotation is the process of adding labels, tags, or metadata to raw data to make it suitable for training supervised machine learning models. Annotating images means drawing bounding boxes around objects; annotating text means labeling entities, sentiment, or intent; annotating product listings means classifying them into categories.

Annotation quality determines model quality. Ambiguous labeling guidelines, inconsistent annotators, and coverage gaps in the annotation set introduce noise that the model learns - resulting in production errors. High-quality annotation requires clear guidelines, multiple annotators for quality checks, and iterative refinement.

Many real-world datasets require annotation before they become useful training data. A raw product listing dataset might need manual category labeling; a property dataset might need human review to validate automated price anomaly flags. Annotation is a significant but often underestimated cost in building ML-powered data products.