safe-synthesizer

Synthetic tabular data generation protects PII by automatically identifying and replacing sensitive information using configurable classification providers while maintaining statistical properties. Differential privacy settings add mathematical guarantees against exposure.

Differential privacy in synthetic data pipelines applies mathematical noise to datasets, ensuring individual records cannot be reverse-engineered while preserving the aggregate statistical properties needed for data science and machine learning.

You generate synthetic tabular data at scale with NeMo by running a synthetic data generation job configured through a local job specification file, leveraging the NeMo Platform Jobs service and GPU-accelerated container environments.

Yes, you need GPU-accelerated container environments and integration with the NeMo Platform Jobs service to execute the safe-synthesizer workflows and generate synthetic tabular data at scale.

Yes, you can validate the quality and utility of synthetic tabular data outputs using the built-in evaluation and reporting feature, which automatically generates comprehensive HTML reports to assess data fidelity.