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DataFreeOpen Source
SDV
Synthetic data generation for tabular, relational, and time-series data
NOASSERTION
ABOUT
Building AI applications requires large, representative datasets, but real production data often contains sensitive information — PII, PHI, financial records — that cannot be shared with developers or used in testing. SDV solves this by learning the statistical patterns of real data and generating synthetic copies that preserve relationships and distributions without exposing actual records, enabling safe data sharing for testing, development, and AI model training.
INSTALL
pip install sdvINTEGRATION GUIDE
1. Generate synthetic tabular data for testing machine learning pipelines without exposing customer PII
2. Create privacy-preserving synthetic datasets that match the statistical distribution of production data
3. Build synthetic time-series data for developing and validating forecasting models
4. Supplement small real datasets with synthetic data to improve AI model training and reduce overfitting
5. Generate relational synthetic data that preserves multi-table relationships for database application testing
TAGS
synthetic-datadata-generationprivacytime-seriestabular-datadeep-learningpython