You work at an ecommerce startup. You need to create a customer churn prediction model. Your company's recent sales records are stored in a BigQuery table. You want to understand how your initial mode

Exporting data to Cloud Storage and loading into a pandas DataFrame on Vertex AI Workbench requires significant manual coding for model building, which is not the quickest or most cost-effective approach for an initial model.

Creating a `tf.data.Dataset` and implementing a deep neural network demands substantial manual effort and expertise, conflicting with the goal of quick iteration and cost minimization for a first model.

Vertex AI AutoML is designed for rapid model development with minimal code, offering features to understand model predictions and optimize for cost by reducing manual effort. Associating BigQuery data with a Vertex AI dataset streamlines the data ingestion for AutoML tabular models.

Exporting data to Cloud Storage and using `tf.data.Dataset` for reading still implies a custom model development path, which is more complex than leveraging AutoML for rapid prototyping and iteration.