7 Lessons I've Learnt From Deploying Machine Learning Models Using ONNX


In this post, we will outline key learnings from a real-world example of running inference on a sci-kit learn model using the ONNX Runtime API in an AWS Lambda function. This is not a tutorial but rather a guide focusing on useful tips, points to consider, and quirks that may save you some head-scratching! The Open Neural Network Exchange (ONNX) format is a bit like dipping your french fries into a milkshake; it shouldn't work but it just does. ONNX allows us to build a model using all the training frameworks we know and love like PyTorch and TensorFlow and package it up in a format supported by many hardware architectures and operating systems. The ONNX Runtime is a simple API that is cross-platform and provides optimal performance to run inference on an ONNX model exactly where you need them: the cloud, mobile, an IoT device, you name it!

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