Hello! I'm currently exploring various modeling approaches and have a question about best practices. Specifically, I'm wondering whether it's better to create separate pipelines (pipeline folders) for each model or to include all the different models within the same pipeline. Keep in mind that they will all be utilizing the same dataset and producing predictions in the same format. Additionally, if there are any other design options I might not have considered, I'd love to hear your thoughts. I understand that the choice may vary depending on the specific use case, but I'm interested in your opinions. Thank you!

It consume the same input dataset but they should still have their own intermediate dataests? Are you aware of namespace pipeline?

Yes, my idea is that they have the same input but support for different intermediate datasets is permitted (some models will need different scaling/normalization techniques)

https://github.com/kedro-org/kedro-viz/tree/main/demo-project You can find it here