To train AI/ML models, you need good data. Agencies also need to protect personally identifiable information, trade secrets, and other confidential information. Traditional methods of synthetic data generation such as rule-based or statistical approaches can be inflexible, limiting the variety and complexity of data they can generate. These techniques can perpetuate biases and produce data that, while statistically similar, lacks realism.
Profile-based Synthetic Data Generation
Unissant’s Profile-based Synthetic Data Generation accelerator produces realistic data that minimizes bias. Developers can construct and test AI/ML models delivering confidentially and data reliability. Our accelerator produces datasets with fully controlled class distribution, randomization methods, and programmatic constraints, all customized to your unique use cases.
benefits
Generate extensive, dependable synthetic data sets
Create realistic data (including synthetic PII) while protecting production data
Configure to generate any volume of use case-specific synthetic data
Build realistic representations of production data for secure training without the risk of production data disclosure
Develop more realistic production data while reducing bias
Incorporate scenarios not covered by source data for more responsive models over time
Demand for artificial intelligence and machine learning solutions continues to surge. At the same time, agencies must secure private and confidential data. Synthetic data creation helps protect data while meeting the demand for AI/ML. However, traditional rule-based or statistical approaches offer limited control, lack realism, and may perpetuate bias.
