MIT Startup DataCebo is Offering A New Tool to Evaluate Synthetic Data
MIT Computer Science & Artificial Intelligence Laboratory (CSAIL) spin-off DataCebo is offering a new tool, called Synthetic Data (SD) Metrics, to help enterprises compare the quality of machine-generated synthetic data by pitching it against real data sets.
SD Metrics is an open-source python library for evaluating model-agnostic tabular synthetic data, that defines metrics for statistics, efficiency and privacy of data. The SDMetrics library is a part of the Synthetic Data Vault (SDV) Project that was first initiated at MIT’s Data to AI Lab in 2016. DataCeba acquired the lab in 2020, and have been developing all aspects of the SDV ever since.
This synthetic data generation ecosystem of libraries was started with the idea to help enterprises create data models for developing new software and applications within the enterprise. It ensures that enterprises can download the packages for generating synthetic data in cases where no data was available or there was a chance of putting data privacy at risk.