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Protopia AI’s Stained Glass Engine™ (SGE) helps AI/ML Engineers protect their sensitive data for AI workloads by creating Stained Glass Transforms (SGTs).
With Stained Glass Engine (SGE) , machine learning engineers can extend their existing training loops to create an SGT. Without modifying the base model code, users can wrap their existing loss functions, optimizers, and models with API calls into SGE. Dive deeper in the SGE documentation.
Optimized Training Process: SGE utilizes PyTorch hooks to manipulate the loss function, manage data flows, and implement memory optimizations during the creation and training of SGTs, ensuring efficient performance without compromising utility
Broad Compatibility: SGE supports any PyTorch module, including Hugging Face Transformers, enabling smooth integration into virtually any training loop, allowing users to extend their AI models with SGTs effortlessly
Hyperparameter Customization: SGE offers hyperparameters that balance the strength of SGTs and the utility of the original model, providing flexible control over the stochastic transformations during the SGT creation process
In this blog, we demonstrate how Protopia AI maximizes the protection of sensitive data when working with popular open-source models on NVIDIA DGX Cloud. We provide a step-by-step exploration of the technology and its implementations, along with performance benchmarks.
This article, the final installment of our three-part series on secure data and impactful AI, outlines key principles and considerations to help you architect data systems and flows for secure and impactful AI.
Welcome back to our three-part series designed by and for leaders in data, information technology, and AI. In the first installment, we tackled the critical issue of overcoming barriers to data accessibility, exploring strategies to unlock the full potential of your data assets while ensuring compliance and security.
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