Introducing Gretel Relational, enabling organizations to generate high-quality synthetic databases while preserving cross-table relationships.

How to utilize Gretel to create high-quality synthetic tabular data that you can use as training data for a classification model in Vertex AI.

Gretel introduces Reinforcement Learning from Privacy Feedback (RLPF), a novel approach to reduce the likelihood of a language model leaking private information.

Gretel and Google Cloud harness the power of synthetic data to accelerate safer adoption of generative AI in the enterprise.

Set up a cutting-edge environment for deep learning with TensorFlow 2.10, PyTorch, Docker, and GPU support.

Gretel's new image synthetics enable you to generate high-quality images at scale. Get started today with our free public preview and let us know what you think!

Learn how to anonymize tabular data to meet GDPR standards using Gretel's synthetic data APIs.

Learn how synthetic image models can address data drift and improve a computer vision model's accuracy in unexpected conditions.

Learn about downstream machine learning tasks and synthetic data with Gretel’s new ACTGAN model and the PyCaret library

Celebrate Taylor Swift's The Eras Tour with lyrics generated with Gretel GPT, our synthetic language model.

Gretel's synthetic data platform overcomes challenges across the data-centric machine learning life cycle to enable ML and AI solutions.

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

Augment or balance your ML datasets in minutes with state-of-the-art generative models.

Gretel is proud to announce the launch of the Synthetic Data Community Discord server.

Announcing the open beta release of our DGAN model type.

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

Generate large volumes of tabular synthetic data at high speed.

Gretel's Source Available License supports long-term growth and innovation in the synthetic data community

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

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.

Get to know some of Gretel’s Applied Science team, their experience building state-of-the-art generative AI models, and advice for aspiring data scientists.

Smart-seeding lets you train a synthetic data model to auto-complete partial records and text.

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.

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

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

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

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

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

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

Augment machine learning datasets with synthetically generated text and labels using an open-source implementation of GPT-3.

Explore state-of-the-art image synthetics for business documents using diffusion models.

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

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

Synthetic data is artificially annotated information that is generated by computer algorithms or simulations, commonly used as an alternative to real-world data.

Machine learning models can be trained to recognize patterns in datasets. By utilizing algorithms, they can learn to make decisions based on these patterns.

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.

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

Five new features that will make synthesizing data easier for busy developers and data scientists.

Promising evidence that state-of-the-art synthetic data models can produce artificial versions of even highly dimensional and complex genomic and phenotypic data.

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

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.

How to enable faster access to data for medical research with statistically accurate, equitable and private synthetic datasets.

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.

How to use Weights & Biases’ ML hyperparameter sweeps tool to optimize the accuracy of your synthetic data.

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

Today, we are thrilled to announce the general availability of Gretel's privacy engineering APIs and services.

Build differentially private synthetic datasets in Python.

How we generated high-quality synthetic time-series data for one of the largest financial institutions in the world.

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

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.

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.

Run privacy engineering workloads at the touch of an API call.

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

I joined Gretel because of the opportunity, people, and problem.

In this post, we will dive into what privacy engineering is, why it’s important, and some of the core use cases we are seeing that are enabled by privacy.

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

Our new configuration templates will help you pick some of the right parameters needed to train your synthetic data models.

Implementing a practical attack to measure un-intended memorization in synthetic data models.

A game-changing AI that will disrupt the cocktail industry and spin the world on its head.

Anonymize data at access time with Gretel and Amazon S3 Object Lambda.

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

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.

Here's what we learned about privacy engineering from 50+ companies and hundreds of developers.

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

What if we could ensure that personal data was protected, benefiting not just the individual but also giving developers faster, worry-free access to data?

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

In this post, we walk through building a data pipeline that will automatically transform datasets so they can be safely used in development environments.

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

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

We are pleased to share that Gretel raised $12M in Series A funding. We're picking up strong momentum in our mission to help developers create safe data.

Create a simple workflow to perform Named Entity Recognition (NER) on sample data using Gretel and load the records into Elasticsearch.

We are releasing new features that make working with data easier by helping you deep dive into records, use blueprints to auto-anonymize data, and more.

We are launching Gretel Blueprints, making it easy to anonymize and balance datasets with just a few clicks.

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

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

Learn how we are improving our product by adding new features that make connecting to Gretel easier, faster and more streamlined.

We recently launched our new entity stream view in Gretel Cloud. See how you can view record streams from tagged entities in your data projects.

Learn more about how Gretel's REST APIs automatically build a metastore that makes it easy to understand what is inside of your data.

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

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

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

Explore how to create a batch interface with the latest version of Gretel Synthetics on Google Colaboratory.

Gretel Outpost is a free integration architecture that automates the steps that a security team would take in assessing the risk or exposure to data.

We decided to examine the privacy preserving capabilities of the Contact Tracing proposal, how it would be implemented, and what privacy concerns exist.

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

Use Gretel Synthetics and Colaboratory’s free GPUs to train a model to automatically generate fake, anonymized data with differential privacy guarantees.

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)

At Gretel, we realized that we can apply machine learning, synthetic data, and formal reasoning to offer provable privacy guarantees for data.