Synthetic Data, Real Privacy: Automating Secure Workflows with Gretel and Amazon SageMaker

Gretel, in partnership with Amazon, has developed a solution for generating synthetic data to address privacy concerns while enabling secure workflows. The process involves using Gretel's multimodal synthetic data generation tools and evaluation metrics, which can be integrated into an automated pipeline utilizing various AWS services such as S3, Lambda, and Secrets Manager. This allows teams to collaborate and build products with safe, shareable data that respects personal privacy. The workflow involves setting up a source S3 bucket for sensitive data, creating a destination S3 bucket for synthetic data, using AWS Lambda to trigger an Amazon SageMaker Notebook instance, and executing Gretel's Transform and Synthetics on the files. The resulting synthetic data can be used by data scientists, ML engineers, or software engineers without exposing sensitive information.