Data science

Learn to prepare tabular data for AI and ML with an end-to-end data cleaning workflow.

Data sharing is central to modern business but entails risks. Synthetic data can enable data sharing while reducing the risk of privacy-compromising linkage attacks.

How we implemented a practical attack on a synthetic data model to validate its ability to protect sensitive information under different parameter settings.

Some of our newest product and technology initiatives that will ensure the Gretel platform continues to grow and evolve with the needs of modern data consumers.

Assessing the efficacy and quality of synthetic data with Gretel Evaluate API.

Synthetic data is algorithmically generated data that mirrors the statistical properties of the dataset it’s based on. Learn how to make high-quality synthetic data.

Gretel’s new synthetic report is here, featuring a high-level score and metrics to help you assess the quality of your synthetic data.

Learn the simplest way to generate synthetic data without setting up your own infrastructure and GPUs.

Take a deep dive on training Gretel’s open-source, synthetic data library to generate electronic health records that protect individual privacy (PII).

An evaluation of the effect of sampling procedures on the quality of synthetic tabular data using Gretel.ai's Synthetic Quality Score (SQS).