Synthetics

Create a fair, balanced, privacy preserving version of the 1994 US Census dataset using gretel-synthetics.

Explore how to create a batch interface with the latest version of Gretel Synthetics on Google Colaboratory.

A practical guide to creating differentially private, synthetic data with Python and TensorFlow.

Our new configuration templates will help you pick some of the right parameters needed to train your synthetic data models.

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.

How synthetic data can help nonprofits improve their business operations and their impact on the people they serve.

We explore the popular open-source package Optuna to demonstrate how you can optimize your model hyperparameters and build the best synthetic model possible.

Create synthetic data that’s safer than ever. Our simple configuration file settings enable you to secure both your data and model from adversarial attacks.

Use synthetic data to improve model accuracy for fraud, cyber security, or any classification task with an extremely limited minority class.

Gretel's Premium SDK now includes detailed reporting that shows you how accurate your synthetic data's statistical distributions and correlations are.