Data science

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

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.

Generate artificial records to balance biased datasets and improve overall model accuracy.

A brief exploration of model soup, the new ensembling technique that takes the average weights of multiple models to improve overall performance.

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

Quickly and safely aggregate geolocation data for location density analysis using a hexagonal grid system.

Create differentially private, synthetic versions of datasets and meet compliance requirements to keep sensitive data within your approved environment.

How to train a FastCUT GAN on public location data from a few cities to predict realistic e-bike locations across the world.

A step-by-step guide to creating high quality synthetic time-series datasets with Python.

We founded Gretel based on our beliefs that data shouldn’t be scary.