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

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

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

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

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

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.

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.

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.