The Synthetic Data Vault

The Synthetic Data Vault (SDV) is an ecosystem of source-available software tools built to help enterprises generate synthetic data. It is currently used by more than 50 fortune 500 companies. Synthetic data is generated by an AI model that has been trained on real data. Data sampled from this model has the same formatting, statistical properties, and patterns as the real data, but can be generated in large quantities on demand and cannot be linked back to real people or real data. The SDV project was launched at MIT in 2018 and is currently maintained and commercialized by DataCebo Inc. It is by far the largest and most comprehensive software system available for synthetic data generation. Enterprises use these tools to develop generative models for their tabular data stores, assess synthetic data, and benchmark these publicly available techniques. We at DataCebo actively develop and maintain these libraries to provide transparency in how generative AI models are built on the data. Through this LinkedIn we cover in depth concepts relevant to synthetic data generation, assessment, generative models, highlights of SDV releases, features. The SDV ecosystem has: Our flagship system for developing generative models for tabular data SDV: https://github.com/sdv-dev/SDV A tool to evaluate synthetic data SDMetrics:https://github.com/sdv-dev/SDMetrics A tool to benchmark different generative modeling techniques SDGym: https://github.com/sdv-dev/SDGym A reversible data transformation (RDT) library to prepare data for modeling RDT: https://github.com/sdv-dev/RDT Generative modeling techniques and tools for tabular data: Copulas: https://github.com/sdv-dev/Copulas CTGAN: https://github.com/sdv-dev/CTGAN DeepEcho: https://github.com/sdv-dev/DeepEcho