The first time I heard about ONNX was during my internship at INRIA. I was working to develop Neural Network Pruning algorithms in the Julia language. There weren't many pre-trained models yet that I could use, so utilizing ONNX to import models developed with other languages and frameworks might have been a solution. In this article, I want to introduce ONNX and explain its enormous potential by also seeing a practical example. ONNX, or Open Neural Network Exchange, is an open-source standard for representing deep learning models. It was developed by Facebook and Microsoft in order to make it easier for researchers and engineers to move models between different deep-learning frameworks and hardware platforms.

AI, machine learning, deep learning, and advanced analytics are being infused into every team and service at Microsoft--understanding customers and the business, operating services, and delivering innovative new features. But doing machine learning at the scale of Microsoft is challenging. ONNX (open neural network exchange format) has bridged the different model formats for ML frameworks (e.g. Learn how Bing, Ads, Speech, Office, Cognitive Services, and others use frameworks like TensorFlow, PyTorch, Scikit-learn, Caffe for training and rely on ONNX Runtime for high performance inferencing. You'll also learn how to use ONNX and ONNX Runtime in your AI application with Azure ML.