Synthetic Data Generation Stack (Python, PyTorch, FastAPI, PostgreSQL, S3) overview

A synthetic data generation platform that uses PyTorch-based generative models (GANs, VAEs, diffusion models) to produce realistic tabular, text, and image datasets that preserve statistical properties of production data without exposing PII. FastAPI exposes generation and validation endpoints while PostgreSQL tracks generation jobs, dataset metadata, and quality metrics. Boto3 manages dataset storage in S3. NumPy and pandas handle data profiling and statistical comparison between real and synthetic distributions. Targeted at ML teams in regulated industries (healthcare, finance, insurance) where production data access is restricted. The tradeoff is fidelity validation — proving that synthetic data adequately represents the real distribution without memorizing individual records requires sophisticated statistical testing and domain expertise.