Synthetics

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

How we generated high-quality synthetic time-series data for one of the largest financial institutions in the world.

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

Train an AI model to create an anonymized version of your dataset using Python, Pandas, and gretel-synthetics.

Augment machine learning datasets with synthetically generated text and labels using an open-source implementation of GPT-3.

How to use Weights & Biases’ ML hyperparameter sweeps tool to optimize the accuracy of your synthetic data.

Take a deep dive on training Gretel’s open-source, synthetic data library to generate electronic health records that protect individual privacy (PII).

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).

In this blog post, we build an ETL pipeline that generates synthetic data from a PostgreSQL database using Gretel’s Synthetic Data APIs and Apache Airflow.