When there's not enough real data to train a model, data scientists are turning to synthetic data — artificial datasets generated from a small sample of real observations. In this episode, Lucas and Luna unpack how a healthcare startup used synthetic data to train a rare-disease diagnostic model when only 200 real patient records existed. They walk through the generation techniques — from simple bootstrapping to GANs and diffusion models — and the hidden risk of 'synthetic bias' where artifacts in generated data fool the model. The episode also covers the open-source libraries turning synthetic data from a research trick into a production tool, and why regulators are starting to pay attention. A concrete look at the practice that lets data scientists do more with less.

#SyntheticData #DataScarcity #GenerativeAI #GANs #DiffusionModels #HealthcareAI #RareDisease #MachineLearning #DataScience #MLOps #BiasInAI #DataAugmentation #OpenSource #SDV #Technology #BusinessPodcast #FexingoBusiness #TheDataSciencePodcast

Keep every episode free: buymeacoffee.com/fexingo