Organizations face a familiar dilemma: how can developers experiment and build models using realistic data without exposing sensitive customer information? Generative adversarial networks (GANs) offer a promising solution by creating synthetic data that mimics real datasets. In this post, we explore a practical approach by training a tabular GAN model in a secure production environment and then deploy that model in a development environment to generate synthetic data for training another mode...
