To unlock the value of machine learning models, organizations must train them on enterprise-specific proprietary data, enabling them to excel in specialized tasks. This is the most challenging task pertaining to training data availability, quality, or sensitivity.
Download this solution brief to learn how synthetic data can be used to:
- Augment sensitive data safe, making it safe to expose to a public model.
- Augment limited datasets to ensure sufficient volume for model training.
- Boost underrepresented classes to remove unwanted bias in downstream tasks.
- Simulate new scenarios to ensure generalizability and performance of intelligent applications.
