Question

A data science team is prototyping several scikit-learn models inside a Vertex AI Workbench notebook. They want to automatically capture parameters, metrics, and artifacts for each training run so that runs can later be compared in a shared experiment view, with minimal manual instrumentation code. Which approach best meets this requirement?

Accepted Answer

Enable Vertex AI Experiments autologging with aiplatform.autolog() before running the training code, associating runs with a named experiment.

Answer

Write each run's parameters and metrics to a BigQuery table and build a Looker Studio dashboard to compare them.

Answer

Save each trained model to a Cloud Storage bucket with a timestamped filename and manually record metrics in a spreadsheet.

Answer

Use Cloud Logging to emit structured log entries for each metric and query them later with Logs Explorer.