A data science team uses Vertex AI Experiments to track training runs. They want to automatically log parameters, metrics, and artifacts for all runs with minimal code changes. Which approach should they take?
Trap 1: Manually log each parameter and metric using…
Manual logging requires code changes and does not capture all artifacts automatically.
Trap 2: Enable Vertex AI Experiments autologging by setting `autolog=True`…
There is no autolog parameter in Vertex AI SDK run context.
Trap 3: Use TensorBoard with tf.keras.callbacks.TensorBoard to log metrics.
TensorBoard logs to local directories, not to Vertex AI Experiments automatically.
- A
Manually log each parameter and metric using `aiplatform.log_metrics()` after each training step.
Why wrong: Manual logging requires code changes and does not capture all artifacts automatically.
- B
Use MLflow autologging by calling `mlflow.autolog()` before the training code and wrap the training script with `mlflow.start_run()`.
MLflow autologging captures parameters, metrics, and artifacts automatically when used with Vertex AI Experiments.
- C
Enable Vertex AI Experiments autologging by setting `autolog=True` in the experiment run context.
Why wrong: There is no autolog parameter in Vertex AI SDK run context.
- D
Use TensorBoard with tf.keras.callbacks.TensorBoard to log metrics.
Why wrong: TensorBoard logs to local directories, not to Vertex AI Experiments automatically.