Gretel, a leading multimodal synthetic data generation platform, announced a Strategic Collaboration Agreement (SCA) with Amazon Web Services (AWS) to accelerate responsible generative artificial intelligence (AI) development that protects sensitive and personal data. Through a newly launched program, selected enterprises will receive direct support from experts at both companies and exclusive access to Gretel’s state-of-the-art synthetic data generation models and privacy tools. The program will enable teams to safely test, train, and fine-tune proprietary large language models (LLMs) using high-quality synthetic data that is private by design.
AWS + Gretel Synthetic Data Accelerator Program for Generative AI
Key benefits for program participants include:
- Direct support from technical experts at Gretel and AWS
- Early access to Gretel’s Tabular LLM
- Opportunity to share research and insights at a generative AI workshop series
“One of the biggest challenges enterprises face today is figuring out how to safely operationalize generative AI applications. High quality synthetic data is the solution. We’re thrilled to collaborate with AWS to empower enterprise developers with the training and quality data they need to scale responsible AI systems,” said Gretel co-founder and CEO Ali Golshan.
The program is open to startups and enterprises across sectors like financial services, healthcare, and the public sector.
The Growing Demand for High Quality Synthetic Data
As generative AI applications are rapidly incorporated into services, the demand for safe, accurate, and timely training data has soared. This is where generating synthetic versions of real-world proprietary datasets that maintain the statistical insights but are not linked to any private individual can be a gamechanger. Synthetic data enables mitigation of privacy risks, augmentation of limited data supplies, simulation of edge cases, and compliance with regulations like GDPR, CCPA, and HIPAA.
Some prominent use cases for synthetic data have already emerged in finance and healthcare. Banks, insurance providers, and cybersecurity teams focused on identifying anomalous activities and novel security threats use synthetic data to train and improve the performance of their fraud detection models. Hospitals and other healthcare providers use synthetic data to test and train machine learning models that support diagnostics and preventative measures that can improve individual patient care.