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run.py

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+from zenml import pipeline, step
+
+@step
+def load_data() -> dict:
+    """Simulates loading of training data and labels."""
+
+    training_data = [[1, 2], [3, 4], [5, 6]]
+    labels = [0, 1, 0]
+    
+    return {'features': training_data, 'labels': labels}
+
+@step
+def train_model(data: dict) -> None:
+    """
+    A mock 'training' process that also demonstrates using the input data.
+    In a real-world scenario, this would be replaced with actual model fitting logic.
+    """
+    total_features = sum(map(sum, data['features']))
+    total_labels = sum(data['labels'])
+    
+    print(f"Trained model using {len(data['features'])} data points. "
+          f"Feature sum is {total_features}, label sum is {total_labels}")
+
+@pipeline
+def simple_ml_pipeline():
+    """Define a pipeline that connects the steps."""
+    dataset = load_data()
+    train_model(dataset)
+
+if __name__ == "__main__":
+    run = simple_ml_pipeline()
+    # You can now use the `run` object to see steps, outputs, etc.