ONNX
Training a machine learning model is only half the battle. Deploying it efficiently, on different hardware, in different runtimes, without performance penalties, is where ONNX (Open Neural Network Exchange) comes in. ONNX is the open standard we use to make trained models portable, fast, and truly production-ready.
What ONNX Solves
- Framework interoperability, Models trained in PyTorch, TensorFlow, Scikit-learn, or XGBoost can all be exported to ONNX format and run with the same ONNX Runtime, no framework lock-in.
- Hardware portability, A single ONNX model runs on CPU, CUDA GPUs, Apple Silicon (via CoreML), ARM processors, and specialised inference hardware like Intel OpenVINO, TensorRT, or edge devices.
- Inference acceleration, ONNX Runtime applies graph optimisations (constant folding, operator fusion, layout transformation) that often deliver 2–5× speedups over framework-native inference without changing the model.
- Language independence, ONNX Runtime has official APIs for Python, C++, C#, Java, and JavaScript, so the same model can be called from any part of your stack.
- Quantisation support, ONNX Runtime supports INT8 and FP16 quantisation, reducing model size by up to 4× and inference latency by 2–3× for production deployments on edge and mobile.
Our ONNX Workflow
After training a model in PyTorch (or another framework), we export it to ONNX, validate the exported graph produces identical outputs to the original, apply ONNX Runtime optimisations, run quantisation if latency or size requirements demand it, and benchmark inference performance under realistic load. The result is a single model artefact that can be deployed anywhere in your infrastructure.
When to Use ONNX
- You need to deploy ML models without shipping a full Python ML framework
- You are targeting edge devices, mobile, or embedded systems
- Inference latency is a hard requirement and you need every millisecond
- You want a hardware-agnostic deployment that can run on whatever infrastructure is cheapest or fastest