Synthetics

Incorporating reasoning traces into synthetic datasets enhances AI transparency and trustworthiness.

How synthetic data is transforming enterprise AI in 2025 by addressing privacy, fine-tuning, and scaling challenges.

Gretel releases an open synthetic dataset to align language models for safety. Get insights into safety datasets.

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

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.

Seed to succeed: use the sample-to-dataset workflow to create diverse, large-scale synthetic datasets tailored to your needs with nothing but a few samples.

Announcing the open beta release of our DGAN model type.

Use Gretel Evaluate classification and regression tasks to validate synthetic data utility

Create privacy-safe synthetic patient data with Gretel, ensuring compliance, secure sharing, and actionable insights for AI and machine learning in healthcare.

How thoughtful, high-quality synthetic data generation, rather than 'indiscriminate' use, can prevent model collapse.

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

Learn how to fine-tune and prompt mpt-7b to generate responses matching popular Twitter personalities with Gretel GPT.

Fine-tune CodeLlama with Gretel's Synthetic Text-to-SQL on BIRDBench, achieving a 36% relative improvement in EX and 38% in VES.

Train financial LLMs with Gretel's Synthetic Text-to-Python dataset to transform natural language into precise, domain-specific Python code for FinTech.

Safely leverage sensitive or proprietary text data for advanced language model training and fine-tuning

Gretel Tabular DP is a fast and powerful new model to generate high quality tabular synthetic data with mathematical guarantees of privacy

Gretel Navigator’s synthetic data generation outperformed OpenAI's GPT-4 by 25.6%, surpassed Llama3-70b by 48.1%, and exceeded human expert-curated data by 73.6%.

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

A new standard for ensuring regulatory compliance and managing the complexities of compound AI systems.

Use Gretel Navigator on Amazon Bedrock to create safe, scalable synthetic data for training AI to understand and execute tool commands.

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

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

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

Gretel's latest open synthetic dataset aims to enhance LLM interactions and contributes to the popular 'awesome-chatGPT-prompts' GitHub repository.

Gretel releases largest open source Text-to-SQL dataset to accelerate AI model training

What The Recent Paper on LLM Reasoning Got Right—And What It Missed.

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.

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

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

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

Gretel partners with Databricks to seamlessly integrate synthetic data workflows and improve model performance for Enterprise AI.

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

‍Gretel today announced that it has successfully achieved Google Cloud Ready - BigQuery designation.

Use Gretel DGAN and Gretel Tuner to generate time series data that accurately mirrors complex business rules and sequences

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

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

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

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

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.

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

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

Announcing the availability of Gretel’s high quality synthetic data generation tools in the AWS Marketplace.

Gretel GPT supports new, state-of-the-art LLMs, and makes it easier for you to trust the privacy and accuracy of LLMs for enterprise use-cases.

Discover how Gretel ACTGAN can help businesses generate synthetic data at scale with improved accuracy, faster training, and reduced memory requirements.

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.

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

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.

Generate large volumes of tabular synthetic data at high speed.

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

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

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

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.

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

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

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.

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

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

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

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.

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

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

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.

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

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

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.

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.

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).

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

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

A look at how using Gretel’s Privacy Filters to immunize synthetic datasets against adversarial attacks can impact machine learning accuracy.

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