Summary
In this chapter, we introduced ONNX, a universal representation of ML models. The benefit of ONNX mostly comes from its model deployment, as it handles environment-specific optimization and conversions for us behind the scenes through ORT. Another advantage of ONNX comes from its interoperability; it can be used to convert a DL model generated with a framework for the other frameworks. In this chapter, we covered conversion for TensorFlow and PyTorch specifically, as they are the two most standard DL frameworks.
Taking another step toward efficient DL model deployment, in the next chapter, we will learn how to use Elastic Kubernetes Service (EKS) and SageMaker to set up a model inference endpoint.
