ONNX
What it is: Open Neural Network Exchange format that enables AI models to work across different frameworks and hardware platforms.
What It Does Best
Framework interoperability. Train in PyTorch, deploy in TensorFlow. Convert models between frameworks without rewriting code or losing accuracy.
Optimize for production. ONNX Runtime delivers 2-10x faster inference than native frameworks. Hardware-specific optimizations for CPU, GPU, mobile, and edge devices.
Future-proof deployments. Don't get locked into one framework. ONNX models work everywhere: cloud, mobile, browsers, IoT devices.
Key Features
Framework support: PyTorch, TensorFlow, scikit-learn, Keras, and more
ONNX Runtime: Fast cross-platform inference engine
Hardware optimization: CPU, GPU, TPU, mobile, edge accelerators
Model zoo: Pre-trained models in ONNX format
Compression: Quantization and pruning for smaller models
Pricing
Free: Open source (Apache 2.0 and MIT licenses)
Commercial: No licensing costs for any use
Cloud: Free software, works on any platform
When to Use It
✅ Need to deploy models cross-platform
✅ Want framework-independent deployment
✅ Optimizing inference speed for production
✅ Deploying to edge devices or mobile
✅ Need faster inference than native frameworks
When NOT to Use It
❌ Training models (use PyTorch/TensorFlow directly)
❌ Model architecture not supported by ONNX
❌ Staying within one framework ecosystem is fine
❌ Research experiments (framework-native better)
❌ Very custom operators not in ONNX spec
Common Use Cases
Production deployment: Fast inference for web services
Mobile AI: Deploy models to iOS and Android
Edge computing: Run AI on IoT devices and embedded systems
Framework migration: Move models between PyTorch and TensorFlow
Model optimization: Quantize and compress for faster serving
ONNX vs Alternatives
vs TorchScript: ONNX cross-framework, TorchScript PyTorch-only
vs TensorFlow Lite: ONNX broader support, TF Lite better for mobile
vs Native framework: ONNX faster inference, native more features
Unique Strengths
Framework-agnostic: Works across all major frameworks
ONNX Runtime speed: Often 2-10x faster than native inference
Industry backing: Microsoft, Facebook, AWS, NVIDIA support
Hardware optimization: Optimized for every major platform
Bottom line: Essential standard for production model deployment. Use ONNX when you need fast, cross-platform inference or want to avoid framework lock-in. Not for training, but unbeatable for serving models efficiently anywhere.