Machine Learning

Synthetic data is artificially annotated information that is generated by computer algorithms or simulations, commonly used as an alternative to real-world data.

How our new Synthetic Data Accelerator Program with AWS will help enterprises scale responsible AI systems fast.

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

Gretel’s privacy-first generative AI is now available to all Azure users as well as select enterprises through the Microsoft for Startups Pegasus Program.

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

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

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

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

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.