Data science

Learn about new features in Gretel, and how those features enabled us to discover personally identifiable information (PII) in a popular Kaggle dataset.

Answers to some questions about synthetic data that audience members submitted during Gretel's talk at The Rise of Privacy Tech’s Data Privacy Week 2022 conference.

By merging breakthrough research on text metrics with new types of embeddings, we produce a reliable metric that is highly correlated with human ratings.

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

Pandas provides so many options of reading data into a DataFrame, here's our short guide to ones that we found most useful.

There is a lot of hype around NLP. In this post, we explore some of the criticisms and how you can use this technology responsibly.

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.

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

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)

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