Gretel Inks Strategic Collaboration Pact with AWS To Advance AI

CIOTechOutlook Team | Wednesday, 08 November 2023, 02:42 IST

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Gretel, a leading multimodal synthetic data generation platform, has signed a Strategic Collaboration Agreement (SCA) with Amazon Web Services (AWS) to accelerate the development of responsible generative artificial intelligence (AI) that safeguards sensitive and personal data. Selected firms will receive direct help from specialists at both companies as well as unique access to Gretel's cutting-edge synthetic data creation algorithms and privacy solutions through a newly created program. The initiative will allow teams to test, train, and fine-tune proprietary large language models (LLMs) in a safe environment utilizing high-quality synthetic data that is designed to be private.
"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.
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, as per business wire. 
Some notable applications of synthetic data have already appeared in finance and healthcare. Synthetic data is used to train and enhance the effectiveness of fraud detection algorithms by banks, insurance companies, and cybersecurity teams focused on discovering unusual activity and emerging security risks. Synthetic data is used by hospitals and other healthcare providers to test and train machine learning models to assist diagnosis and preventative actions that can improve individual patient care.

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