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Stained Glass Engine™

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

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