Professional Machine Learning Engineer

You have a custom job that runs weekly on Vertex AI. The job uses a proprietary ML workflow that generates the datasets, models, and custom artifacts, then sends them to a Cloud Storage bucket. Many different versions of the datasets and models have been produced. Because of compliance requirements, your company must track which model was used to make a particular prediction and must be able to access the artifacts for every model. How should you configure your workflows to satisfy these requirements?