The Synthetic Data Vault (SDV) enables end users to easily generate Synthetic Data for different data modalities, including single table, relational and time series data. Users can then use the statistical model to generate a synthetic dataset. Synthetic data can then be used to supplement, augment and in some cases replace real data when training machine learning models. Additionally, it enables the testing of machine learning or other data dependent software systems without the risk of exposure that comes with data disclosure. Underneath the hood it uses a unique hierarchical generative modeling and recursive sampling techniques.

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