Introducing Gretel Amplify

The Gretel Amplify model is a new tool for rapidly generating large volumes of tabular synthetic data using statistical models and hyper-efficient multi-processing implementation. It runs on CPU and can generate data up to 1000x faster than deep learning-based generative models, enabling users to create large numbers of synthetic records very quickly. Amplify is effective at learning and recreating distributions and correlations but typically has a 10-15% drop in synthetic data accuracy compared to Gretel's deep learning models. Some use cases for Amplify include creating synthetic data for load testing applications, mimicking real-world data for pre-production environments, and generating synthetic examples to test machine learning model generalization capabilities.

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