Back to articles
Data & Analytics
Synthetic Data Generation for AI Training
Generate synthetic data to augment training sets, protect privacy, and reduce data collection costs.
Ankit 11 min readNovember 23, 2024
The Case for Synthetic Data
Synthetic data - artificially generated data that mimics real data - addresses data scarcity, privacy, and bias challenges.
Benefits
Privacy
- No personal information
- Regulatory compliance
- Safe sharing
Availability
- Create rare scenarios
- Balance datasets
- Overcome scarcity
Cost
- Reduce collection costs
- Faster iteration
- Flexible generation
Generation Methods
Statistical Methods
- Distribution sampling
- Gaussian copulas
- SMOTE for imbalance
Deep Learning
- VAEs
- GANs
- Diffusion models
Simulation
- Physics-based
- Agent-based
- Game engines
Applications
Computer Vision
- Training data augmentation
- Edge case generation
- Domain adaptation
NLP
- Text augmentation
- Translation pairs
- Dialogue generation
Tabular Data
- Privacy-preserving analytics
- Testing
- Simulation
Quality Considerations
Fidelity
- Statistical similarity
- Feature preservation
- Relationship capture
Utility
- Model performance
- Task suitability
- Downstream value
Privacy
- Re-identification risk
- Differential privacy
- Membership inference
Tools and Platforms
- Gretel
- Mostly AI
- Synthesized
- NVIDIA Omniverse
Conclusion
Synthetic data is an increasingly important tool in the ML practitioner's toolkit.
Synthetic DataData AugmentationPrivacy
Next step
Need help putting this into production?
Our senior architects build AI systems that run in production, not demos. The call is 30 minutes and there's no pitch.
Book a discovery call →