Transform

Create privacy-safe synthetic patient data with Gretel, ensuring compliance, secure sharing, and actionable insights for AI and machine learning in healthcare.

A walkthrough of our new multi-table transform and multi-table synthetics notebooks, which can be used independently or simultaneously.

Ensure data quality and privacy by applying flexible de-identification and rule-based transformations to real and synthetic datasets.

We are launching Gretel Blueprints, making it easy to anonymize and balance datasets with just a few clicks.

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

Anonymize data at access time with Gretel and Amazon S3 Object Lambda.

In this post, we walk through building a data pipeline that will automatically transform datasets so they can be safely used in development environments.