Model packaging is a crucial aspect of deploying machine learning models effectively. It involves the process of preparing a model so that it can be easily shared, reused, and integrated into various applications. This includes defining the model’s dependencies, configurations, and any necessary metadata that allows it to function correctly in different environments.

One of the key frameworks mentioned in the context of model packaging is ONNX (Open Neural Network Exchange), which facilitates interoperability between different machine learning frameworks. This means that models trained in one framework can be converted and used in another, enhancing flexibility and collaboration across teams and platforms ¹.

Additionally, understanding how to create and publish packages, such as Python packages, is essential for distributing your model. This includes using tools like PyPI for sharing your packages and understanding formats like Python eggs and wheels, which help in managing dependencies and installations ².

If you have more specific questions about model packaging or related topics, feel free to ask!