Question

Two data science teams share a Vertex AI Workbench environment to prototype churn models against a large BigQuery table. During prototyping, one team discovers that a colleague's earlier notebook produced a model with 4% higher AUC, but nobody can reproduce it because the underlying training data was overwritten by a nightly ETL job and the feature engineering code was iterated in place. Leadership asks you to establish a practice so future prototype results can be reliably reproduced and compared across teams. Which approach best addresses both data and model reproducibility?

Accepted Answer

Snapshot each training dataset as a BigQuery table with a timestamp suffix, log each run to Vertex AI Experiments capturing parameters and metrics, and register resulting models in Vertex AI Model Registry with links to the dataset snapshot.

Answer

Have each team export their notebook to a shared Cloud Storage bucket after every run and rely on the notebook cell outputs to document metrics and data used.

Answer

Enable BigQuery table expiration on the ETL output tables so historical data is retained longer, and email AUC results to the group after each experiment.

Answer

Increase the Workbench instance disk size so all intermediate CSV files are stored locally, and use Git commit messages to record which model achieved the best AUC.