Imagine seamlessly training, testing, and fine-tuning bespoke machine learning models, without the risk of jeopardizing individual privacy or breaching trade secrets? 

The path there would require leveraging advanced data security mechanisms and privacy-enhancing technologies, coupled with state of the art deep neural networks for data generation and the right infra to scale. Today, we’re thrilled to provide that comprehensive solution, through a strategic collaboration with AWS, and a new Synthetic Data Accelerator Program designed to support privacy-centric generative AI development. 

AWS + Gretel Synthetic Data Accelerator Program for GenAI

This program isn’t merely a step but a giant leap towards developing responsible AI applications. Participants will gain access, insights, and opportunities that include:

• Direct support from Gretel and AWS technical experts
• Early access to Gretel Navigator, the first AI system designed for generating, editing, and augmenting tabular data via natural language or SQL prompts
• A chance to share research and insights at a generative AI workshop series

The program is open to startups and enterprises across industries including financial services, healthcare, and the public sector.

Why 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 game-changer. 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. It is an indispensable tool for modern developers.

Emerging Applications Across Domains

Industries have already discovered high value applications for synthetic data that highlight its transformative potential. Healthcare providers harness synthetic data to fine-tune machine learning models, enhancing diagnostic and preventive measures. Financial institutions employ it to amplify the accuracy and reliability of their fraud detection models and for sharing data safely across internal and external teams. The possibilities are endless.

However, the shift towards synthetic data comes with its own prerequisites. "Asking data-driven developers to exchange real-world data for synthetics requires they not only have a deep dedication to privacy, but also access to simple, intuitive solutions that return value immediately. Gretel provides all of the above," said Chris Hymes, CISO at Riot Games. This point highlights our dedication to simplifying the process for developers and teams to work with secure data.

Ultimately, synthetic data is a catalyst for responsible AI innovation. "The combination of synthetic data generation tools with AWS’s secure infrastructure and flexibility, enables businesses to confidently explore and safely advance their generative AI initiatives,” said Stephen Baker, AWS Director of Generative AI. “This is a path forward for innovators who seek a modern and more sustainable approach to building with data.”

Let's Build Together

Gretel’s collaboration with AWS promises to be a lighthouse for AI engineers and developers who are committed to building with high quality data that is private by design. By enabling teams to safely test, train, and fine-tune proprietary large language models (LLMs) and other AI applications using synthetic data, we can foster an ecosystem of responsible innovation. 

Interested in being part of this amazing initiative? Applications for the Synthetic Data Accelerator Program are now open for startups and enterprises everywhere. Sign up and let’s build together.