Universal Standard for AI Model Interoperability
WIA-AI-014
εΌηδΊΊι Β· Benefit All Humanity
Seamlessly convert between ONNX, TensorFlow, PyTorch, and other major frameworks. Universal format for model exchange and deployment across different platforms.
Built-in optimization and quantization support. Efficient model serialization with minimal overhead and maximum runtime performance across hardware.
Comprehensive metadata support including versioning, training info, hyperparameters, and deployment requirements for complete model lineage.
Security, validation, and compliance built-in. Support for model signatures, checksums, and audit trails for production deployments.
| Format | Framework | Use Case | WIA-AI-014 Support |
|---|---|---|---|
ONNX |
Framework Agnostic | Cross-platform deployment | β Full Support |
SavedModel |
TensorFlow | TF Serving, TFLite | β Full Support |
TorchScript |
PyTorch | Production PyTorch | β Full Support |
CoreML |
Apple | iOS/macOS deployment | β Conversion Support |
TFLite |
TensorFlow | Mobile/Edge devices | β Conversion Support |
GGUF |
llama.cpp | LLM quantization | β LLM Support |
Standardize model artifacts across your entire ML pipeline - from training to deployment. Support for versioning, A/B testing, and rollback with complete model lineage tracking.
Deploy the same model to AWS SageMaker, Google Vertex AI, Azure ML, and on-premise infrastructure without modification. Single format, multiple targets.
Convert and optimize models for mobile, IoT, and edge devices. Automatic quantization and compression while maintaining accuracy and performance.
Share and distribute models with confidence. Standardized format enables model marketplaces, transfer learning, and collaborative AI development.
Publish models with complete metadata for reproducible research. Include training data references, hyperparameters, and evaluation metrics.
Meet compliance requirements with built-in audit trails, model signatures, and access controls. Track model lineage from data to deployment.