Gretel Blog

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

This article clarifies some common misconceptions about differential privacy and what it guarantees.

Quickly and safely aggregate geolocation data for location density analysis using a hexagonal grid system.

Use Gretel.ai's APIs to continuously detect and protect sensitive data including credit cards, credentials, names, and addresses.

Use Gretel’s NLP setting to label PII including people names and geographic locations in free text.

Today we announced that Gretel raised $50 million in funding to help us advance our mission to bring “privacy by design” to all developers.

By merging breakthrough research on text metrics with new types of embeddings, we produce a reliable metric that is highly correlated with human ratings.

There is a lot of hype around NLP. In this post, we explore some of the criticisms and how you can use this technology responsibly.

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.

In this blog, we will use Fluent Bit to collect logs from AWS EKS cluster applications.

In this blog post, we build an ETL pipeline that generates synthetic data from a PostgreSQL database using Gretel’s Synthetic Data APIs and Apache Airflow.

Train an AI model to create an anonymized version of your dataset using Python, Pandas, and gretel-synthetics.