Gretel Blog

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. Get the full data simulation definition.

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.

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

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