Synthetics

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.

How synthetic data can help nonprofits improve their business operations and their impact on the people they serve.

Everything you need to know about anonymizing data and the techniques for mitigating privacy risks.

Announcing the open beta release of our DGAN model type.

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

Test data generation is the process of creating new data that replicates an original dataset. Here’s how developers and data engineers use it.

Benchmark is your toolkit to evaluate any synthetic data algorithm on any production dataset

Discover how to efficiently use Gretel's platform for prompting Llama-2 on large datasets, whether you're completing answers, generating synthetic text, or labeling.

Gretel Workflows orchestrate synthetic data generation, ensuring users have accurate, up-to-date data for software development, analytics, and ML/AI.