[Feedback] docs/components/pipelines/user-guides/core-functions/build-advanced-pipeline.md

[samoshyn]

It appears that the pipeline is currently using hard-coded values (True/False) for the scaler parameters in the normalize_dataset component:

@dsl.pipeline(name='iris-training-pipeline')
def my_pipeline(
    standard_scaler: bool,
    min_max_scaler: bool,
    neighbors: List[int],
):
    create_dataset_task = create_dataset()

    normalize_dataset_task = normalize_dataset(
        input_iris_dataset=create_dataset_task.outputs['iris_dataset'],
        standard_scaler=True,
        min_max_scaler=False)

Since the pipeline already defines standard_scaler and min_max_scaler as input arguments, it would be more flexible to pass these arguments directly to the task.

[varodrig]

/transfer pipelines