Contact Us
Back to Insights
Data & Analytics

Synthetic Data Generation for AI Training

Generate synthetic data to augment training sets, protect privacy, and reduce data collection costs.

Rottawhite Team11 min readNovember 23, 2024
Synthetic DataData AugmentationPrivacy

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.

    Share this article:

    Related Articles

    AI Ethics

    AI and Privacy: Data Protection Best Practices

    Protect user privacy in AI applications. GDPR compliance, data anonymization, and privacy-preserving ML.

    Read more
    Data & Analytics

    Data Preparation for Machine Learning: Best Practices

    Master data cleaning, feature engineering, and preprocessing techniques for better ML models.

    Read more
    Data & Analytics

    Feature Engineering: Techniques for Better Models

    Learn advanced feature engineering techniques to improve model performance and accuracy.

    Read more

    Need Help Implementing AI?

    Our team of AI experts can help you leverage these technologies for your business.

    Get in Touch