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You are developing an ML model that predicts the cost of used automobiles based on data such as location, condition, model type, color, and engine/battery efficiency. The data is updated every night. Car dealerships will use the model to determine appropriate car prices. You created a Vertex AI pipeline that reads the data, splits the data into training/evaluation/test sets, performs feature engineering, trains the model using the training dataset, and validates the model using the evaluation dataset. You need to configure a retraining workflow that minimizes cost. What should you do?