> Runs the training script from your “new_eta_model” branch and generates the model artifact
This is interesting, I am using github action for CI. The github action has its own server specification to run the CI. I wonder if we can train a model in seperate machine so that GH action server is not heavy to train model
> Runs the training script from your “new_eta_model” branch and generates the model artifact
This is interesting, I am using github action for CI. The github action has its own server specification to run the CI. I wonder if we can train a model in seperate machine so that GH action server is not heavy to train model
> Run unit tests, for example, to make sure you feature engineering functions work as expected. For that, you can use a library like pytest
Should we create unit test for all functions?
Do you have any best practices for writing unit tests? AFAIK, this is a common practice in Software Engineering but not in MLE