AI

Announcing the open beta release of our DGAN model type.

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

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

Gretel's synthetic GSM8k dataset shows an 84% improvement for AI Reasoning tasks vs synthetic data generated without the Reflection technique.

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

How to safely fine-tune LLMs on sensitive medical text for healthcare AI applications using Gretel and Amazon Bedrock

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

Gretel releases industry standard synthetic tabular data privacy evaluation and risk-based scoring system.

How our new Synthetic Data Accelerator Program with AWS will help enterprises scale responsible AI systems fast.

Use Gretel's synthetic data platform to replace, augment, or balance training datasets within MLOps pipelines like Vertex AI, Azure ML, and Amazon SageMaker.

Learn how to generate question-answer pairs from documents using a unique application built using Gretel Navigator.

Effective strategies for leveraging high-quality synthetic data to improve RAG model performance.

Leverage Gretel’s New Ultra-Fast and Fully Flexible De-Identification and Rule-Based Transformation Solution for HIPAA Compliance.

How differential privacy can generate provably private synthetic text data for a variety of enterprise AI applications.

How to automatically generate missing tabular data that maintains contextual relevance.

Automate hyperparameter sweeps to create the best synthetic data for your task 🧹

Gretel’s privacy-first generative AI is now available to all Azure users as well as select enterprises through the Microsoft for Startups Pegasus Program.

Celebrating Gretel's latest innovations by diving into the future of multimodal synthetic data, and our Model Playground and Tabular LLM.

Discover the streamlined Gretel Python SDK. Start building with synthetic data in just 3 lines of code 🚀

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.

Create high-quality synthetic datasets of conversational dialogs safely tuned on your private, sensitive data with Gretel.

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

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

Gretel’s suite of privacy-enhancing tools and generative AI models are now available on Google Cloud Marketplace.

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!

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

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)

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

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

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

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.

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

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

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

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.

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

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.

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

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

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

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.

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).

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