AI

Gretel Outpost is a free integration architecture that automates the steps that a security team would take in assessing the risk or exposure to data.

Set up a cutting-edge environment for deep learning with TensorFlow 2.10, PyTorch, Docker, and GPU support.

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

Use Gretel Synthetics and Colaboratory’s free GPUs to train a model to automatically generate fake, anonymized data with differential privacy guarantees.

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.

Explore state-of-the-art image synthetics for business documents using diffusion models.

Generate synthetic time series data with Gretel.ai’s open-source PyTorch implementation of DoppelGANger.

This article clarifies some common misconceptions about differential privacy and what it guarantees.

We’re going to train and build our synthetic dataset off of a real-time public feed of e-bike ride-share data called the GBFS (General Bike-share Feed)

An interview with one of Gretel's users on why medical practitioners turn to synthetic data when overcoming challenges with clinical data.