Synthetics

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.

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

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

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

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

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.

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

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%.

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.