You've utilized Vertex AI Workbench notebooks to construct a TensorFlow model, and the notebook follows these steps: Fetching data from Cloud Storage, Employing TensorFlow Transform for data preprocessing, Utilizing native TensorFlow operators to define a sequential Keras model, Conducting model training and evaluation using model.fit() within the notebook instance, and Storing the trained model in Cloud Storage for serving. Your objective is to orchestrate a weekly model retraining pipeline with minimal cost, refactoring, and monitoring efforts. How should you proceed to achieve this?