You are a member of a data science team at a bank, tasked with building an ML model for predicting loan default risk. Your dataset, consisting of hundreds of millions of cleaned records, is stored in a BigQuery table. Your objective is to create and evaluate multiple models using TensorFlow and Vertex AI while ensuring that the data ingestion process is efficient and scalable. To achieve this, what steps should you take to minimize bottlenecks during data ingestion?