Your organization’s marketing team wants to send biweekly scheduled emails to customers that are expected to spend above a variable threshold. This is the first ML use case for the marketing team, and you have been tasked with the implementation. After setting up a new Google Cloud project, you use Vertex AI Workbench to develop model training and batch inference with an XGBoost model on the transactional data stored in Cloud Storage. You want to automate the end-to-end pipeline that will securely provide the predictions to the marketing team, while minimizing cost and code maintenance. What should you do?

Vertex AI Pipelines and Cloud Storage are cost-effective and secure solutions. The solution requires the least number of code interactions because the marketing team can update the pipeline and schedule parameters from the Google Cloud console. Cloud Composer is not a cost-efficient solution for one pipeline because its environment is always active. In addition, using BigQuery is not the most cost-effective solution. The marketing team would have to enter the Vertex AI Workbench instance to update a pipeline parameter, which does not minimize code interactions. Also, using email to send personally identifiable information (PII) is not a recommended approach.