Machine Learning

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

How we implemented a practical attack on a synthetic data model to validate its ability to protect sensitive information under different parameter settings.

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.

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

Gretel introduces Reinforcement Learning from Privacy Feedback (RLPF), a novel approach to reduce the likelihood of a language model leaking private information.

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

Use Gretel's synthetic data platform to replace, augment, or balance training datasets within MLOps pipelines like Vertex AI, Azure ML, and Amazon SageMaker.