Step 4: Configuration Define the resource class ModelTrainingConfig(ConfigurableResource): """Model training configuration""" learning_rate: float n_estimators: int random_state: int Load the config in the asset @asset() def price_prediction_model( train_config: ModelTrainingConfig, train_data: pd.DataFrame, test_data: pd.DataFrame ) -> None: """Price prediction model.""" GradientBoostingRegressor( learning_rate=train_config.learning_rate, n_estimators=train_config.n_estimators, random_state=train_config.random_state, ), Register the training config from ames_housing.resources.training_config import ModelTrainingConfig definitions = Definitions( ... resources={ ... "train_config": ModelTrainingConfig( learning_rate=0.1, n_estimators=100, random_state=42, ), }, )