Data science

Synthetic data is algorithmically generated data that mirrors the statistical properties of the dataset it’s based on. Learn how to make high-quality synthetic data.

Announcing the open beta release of our DGAN model type.

How we implemented a practical attack on a synthetic data model to validate its ability to protect sensitive information under different parameter settings.

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.

Data simulation is the process of using large quantities of data to predict events and validate models. Get the full data simulation definition.

How Gretel's data quality analysis tools for evaluating generated text can help you optimize the performance LLMs, like the Llama-2 model.

How combining agent-based methods, LLMs, and synthetic data enables natural language queries for databases and data warehouses, sans SQL.

Let’s fine tune and prompt a large language model to swap the sentiment of product reviews!

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

An interview with one of Gretel's users on why medical practitioners turn to synthetic data when overcoming challenges with clinical data.

Learn the simplest way to generate synthetic data without setting up your own infrastructure and GPUs.

We’re going to train and build our synthetic dataset off of a real-time public feed of e-bike ride-share data called the GBFS (General Bike-share Feed)

Generate synthetic time series data with Gretel.ai’s open-source PyTorch implementation of DoppelGANger.

Data sharing is central to modern business but entails risks. Synthetic data can enable data sharing while reducing the risk of privacy-compromising linkage attacks.

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.

Pandas provides so many options of reading data into a DataFrame, here's our short guide to ones that we found most useful.

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

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

Answers to some questions about synthetic data that audience members submitted during Gretel's talk at The Rise of Privacy Tech’s Data Privacy Week 2022 conference.

Learn about new features in Gretel, and how those features enabled us to discover personally identifiable information (PII) in a popular Kaggle dataset.

We founded Gretel based on our beliefs that data shouldn’t be scary.

A step-by-step guide to creating high quality synthetic time-series datasets with Python.

How to train a FastCUT GAN on public location data from a few cities to predict realistic e-bike locations across the world.

Create differentially private, synthetic versions of datasets and meet compliance requirements to keep sensitive data within your approved environment.

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

Use synthetic data to improve model accuracy for fraud, cyber security, or any classification task with an extremely limited minority class.

A brief exploration of model soup, the new ensembling technique that takes the average weights of multiple models to improve overall performance.

Generate artificial records to balance biased datasets and improve overall model accuracy.

Some of our newest product and technology initiatives that will ensure the Gretel platform continues to grow and evolve with the needs of modern data consumers.

A practical guide to creating differentially private, synthetic data with Python and TensorFlow.

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

The Gretel research team's favorite trends and takeaways from the NeurlPS 35th Annual Conference on Neural Information Processing Systems.

Beta2 for Gretel.ai is all about delivering privacy engineering as a service through clean, simple APIs.

An evaluation of the effect of sampling procedures on the quality of synthetic tabular data using Gretel.ai's Synthetic Quality Score (SQS).

Assessing the efficacy and quality of synthetic data with Gretel Evaluate API.

Take a deep dive on training Gretel’s open-source, synthetic data library to generate electronic health records that protect individual privacy (PII).