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The Synthetic Data Vault (SDV) is a Synthetic Data Generation ecosystem of libraries that allows users to easily learn single-table, multi-table and timeseries datasets to later on generate new Synthetic Data that has the same format and statistical properties as the original 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 several probabilistic graphical modeling and deep learning based techniques. To enable a variety of data storage structures, we employ unique hierarchical generative modeling and recursive sampling techniques.
Features
- Handling of multiple data types and missing data with minimum user input
- Support for pre-defined and custom constraints and data validation
- Definition of entire multi-table datasets metadata with a custom and flexible JSON schema
- Using Copulas and recursive modeling techniques
- Conditional sampling based on contextual attributes
- An easy to use Evaluation Framework to evaluate the quality of your synthetic data with a single line of code
Project Samples
