Complete Technical Guide
WIA-AI-014 Standard
εΌηδΊΊι Β· Benefit All Humanity
Understand the fundamentals of neural network serialization, model formats, and why standardization matters for AI interoperability. Learn about tensor representation, graph structures, and metadata requirements.
Read Chapter 1 βComprehensive exploration of the Open Neural Network Exchange (ONNX) format. Learn about operator sets, graph representation, model optimization, and cross-framework compatibility.
Read Chapter 2 βMaster TensorFlow's SavedModel format, including signature definitions, serving infrastructure, variable management, and deployment strategies for production environments.
Read Chapter 3 βDeep dive into PyTorch's TorchScript for production deployment. Learn about tracing, scripting, JIT compilation, and optimization techniques for PyTorch models.
Read Chapter 4 βPractical guide to converting models between frameworks. Learn conversion strategies, handle edge cases, preserve accuracy, and optimize for different target platforms.
Read Chapter 5 βAdvanced techniques for model optimization including quantization, pruning, knowledge distillation, and hardware-specific optimizations for deployment efficiency.
Read Chapter 6 βBest practices for model metadata, versioning strategies, lineage tracking, and documentation. Essential for reproducibility and production model management.
Read Chapter 7 βProduction deployment patterns, MLOps integration, monitoring, governance, security, and compliance for enterprise-scale AI systems using WIA-AI-014.
Read Chapter 8 β