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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
How Stained Glass helps financial institutions and and AI builders run AI with sensitive data without exposure while improving GPU utilization and cost.
Protopia Stained Glass Transform (SGT) is now compatible with NVIDIA NIM microservices. Together, SGT and NIM microservices enable organizations to run sensitive, high-value workloads on efficient shared infrastructure based on the NVIDIA AI Factory for Government reference design.
Your AI hosting configuration can make or break the ROI of your use case. Learn how to balance the needs of the CISO and the CFO with secure, efficient AI infrastructure powered by SGT.