Scaling your Machine Learning Projects Seamlessly

# ZenML pipeline syntax
from zenml import pipeline, step

@step
def data_preprocessing(data):
    ... # preprocessing logic

@step
def model_training(preprocessed_data):
    ... # model training logic

@pipeline
def ml_pipeline(data):
    preprocessed_data = data_preprocessing(data)
    trained_model = model_training(preprocessed_data)
    return trained_model

# Kedro pipeline syntax
from kedro.pipeline import Pipeline, node

def data_preprocessing(data):
    ... # preprocessing logic

def model_training(preprocessed_data):
    ... # model training logic

def create_pipeline(**kwargs):
    return Pipeline(
        [
            node(
                func=data_preprocessing,
                inputs="raw_data",
                outputs="preprocessed_data",
                name="preprocess_data",
            ),
            node(
                func=model_training,
                inputs="preprocessed_data",
                outputs="trained_model",
                name="train_model",
            ),
        ]
    )