Synthetic data is artificially generated data that mimics the statistical properties of real-world data, used to train or test machine learning models when real data is scarce, sensitive, or imbalanced.
Synthetic data can fill gaps where real data is limited or privacy-restricted, help balance underrepresented cases in a training set, and allow testing without exposing sensitive real records.
Many production AI systems blend real-world web-collected data with synthetic examples to improve coverage of edge cases the real data doesn’t sufficiently represent.
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