Alex Watson

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

Building an end-to-end evaluation pipeline for RAG systems using synthetic data generation.

Gretel releases a new synthetic financial document dataset to empower AI developers in building customized and highly performant sensitive data detection systems.

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.

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%.

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

Leveraging Gretel's privacy-preserving synthetic data generation platform to fine-tune Azure OpenAI Service models in the financial domain.

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

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

How to use Gretel Navigator for generating diverse, high-quality training data to create better language models.

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

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.

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

Here's how a hospital uses Gretel to help forecast staffing and resource needs for their emergency care unit, and to identify emerging trends in outbreaks.

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.

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

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

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

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

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)

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

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.

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

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

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.

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

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

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

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

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.

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

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

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

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

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

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.

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