Gretel Blog

Smart-seeding lets you train a synthetic data model to auto-complete partial records and text.

Can synthetic data really be used in machine learning? We explore the utility of synthetic data created from popular datasets and tested on popular ML algorithms.

Here's what we learned about privacy engineering from 50+ companies and hundreds of developers.

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

Train an AI model to create an anonymized version of your dataset using Python, TensorFlow, and Gretel-synthetics.

What if we could ensure that personal data was protected, benefiting not just the individual but also giving developers faster, worry-free access to data?

Set up a cutting edge environment for deep learning with TensorFlow 2.4 and GPU support.

Use Gretel.ai's APIs to continuously detect and protect sensitive data including credit cards, credentials, names, and addresses.

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

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