Open Neural Network Exchange (ONNX) is an open format designed to represent ML models. It provides a standardized way to interchange models between various deep learning frameworks, such as PyTorch and TensorFlow.

The goal of ONNX is to enable seamless interoperability and portability of models across different platforms and tools. It allows users to train models using one framework and deploy them in another without the need for extensive model conversion or reimplementation.

ONNX defines a common intermediate representation called the ONNX format, which captures the structure of the model, the types of inputs and outputs, as well as the operations performed by the model. This format can be used to serialize models and store them as ONNX files, which can then be loaded by frameworks that support ONNX.

By using ONNX, developers can leverage the strengths of different deep learning frameworks and take advantage of a wider range of tools and libraries. It simplifies the process of model development and deployment, as it provides a unified format for model exchange and collaboration across the ML community.

Using ONNX with ML.NET

In this lesson, we'll learn how to integrate ONNX with ML.NET. We'll do so with the help of the following playground:

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