Generative Data Synthesis for Model Training

Create synthetic tabular, image, or text datasets to augment training data, balance classes, or simulate rare scenarios while preserving statistical fidelity.

• Tabular data synthesis (CTGAN, TVAE)
• Image synthesis with diffusion models
• Privacy-preserving synthetic data (DP-SGD)
• Data augmentation pipelines
• Quality metrics (KSTest, coverage)

• Bootstrap ML projects with limited data
• Balance underrepresented classes
• Protect PII while retaining data utility
• Simulate extreme scenarios for stress testing