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.

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.

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

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

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

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

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

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

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.

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