Amy Steier

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.

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

How to de-identify a relational database for demo or pre-production testing environments while keeping the referential integrity of primary and foreign keys intact.

Gretel releases industry standard synthetic tabular data privacy evaluation and risk-based scoring system.

Learn to prepare tabular data for AI and ML with an end-to-end data cleaning workflow.

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

Gretel's Premium SDK now includes detailed reporting that shows you how accurate your synthetic data's statistical distributions and correlations are.

Create synthetic data that’s safer than ever. Our simple configuration file settings enable you to secure both your data and model from adversarial attacks.

We explore the popular open-source package Optuna to demonstrate how you can optimize your model hyperparameters and build the best synthetic model possible.

Look at how FastText word embeddings can help to quickly understand new datasets, and build more consistent labels for your own data.

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

A look at how using Gretel’s Privacy Filters to immunize synthetic datasets against adversarial attacks can impact machine learning accuracy.