Question

A data science team of six people at a healthcare analytics company needs to collaborate on model prototyping using Vertex AI Workbench notebooks. Their requirements are: each member should have an isolated compute environment, all notebook code must be version-controlled and peer-reviewed before merging, and no protected health information (PHI) should be persisted in notebook outputs committed to the repository. Which approach best satisfies all three requirements?

Accepted Answer

Give each member their own Vertex AI Workbench instance, integrate the notebooks with a Git repository that enforces pull-request reviews, and configure a pre-commit hook or nbconvert step that strips cell outputs before committing.

Answer

Provision a single large managed notebook instance shared by all six members, and rely on Cloud Source Repositories snapshots taken hourly to track changes.

Answer

Have all members work in a shared Colab Enterprise runtime, export finished notebooks to Cloud Storage nightly, and manually review the exported files in a weekly meeting.

Answer

Provision individual notebook instances and enable automatic checkpoint saving to a shared bucket, using bucket object versioning as the change history and code review mechanism.