"There are 14 competing standards"

[tornikeo]

I've just finished reading the beyond-jupyter, and while I appreciate the effort, I don't understand this:

How would I convince someone that writing this:

    @classmethod
    def create_logistic_regression_orig(cls):
        return SkLearnLogisticRegressionVectorClassificationModel(solver='lbfgs', max_iter=1000) \
            .with_feature_generator(FeatureGeneratorTakeColumns(cls.COLS_USED_BY_ORIGINAL_MODELS)) \
            .with_feature_transformers(DFTSkLearnTransformer(StandardScaler())) \
            .with_name("LogisticRegression-orig")

Is more maintainable, and easier to learn and extend for new developers than writing a custom fastapi->pandas->sklearn->fastapi pipeline for each new experiment? Wouldn't there be a lot less boilerplate code in that kind of codebase?