Google Professional Machine Learning Engineer Question 439
Select 3Google Cloud PlatformYou are working on a machine learning pipeline using Vertex AI, and you want to track the lineage of your experiments, including datasets, models, and metrics. How can Vertex ML Metadata help you achieve this?
- A
It allows you to log artifacts, executions, and lineage information automatically.
- B
It enables you to store and serve trained models for prediction requests.
- C
It provides a way to visualize the relationships between datasets, models, and training jobs.
- D
It automatically tunes hyperparameters for your machine learning models.
- E
It helps in auditing and debugging ML workflows by tracking the history of executions and their inputs/outputs.
Show answer and explanation
Correct answers: A, C, E
Explanation
Vertex ML Metadata is a powerful tool for tracking and managing the metadata of your ML workflows. It helps log artifacts, executions, and lineage information while also enabling visualization of relationships between components. Additionally, it facilitates auditing and debugging by tracking the history of executions and their inputs/outputs. However, it is not used for hyperparameter tuning or serving models for prediction.
- A. Correct.
Correct. Vertex ML Metadata collects and stores metadata about artifacts and executions, helping you track lineage information automatically.
- B. Incorrect.
Incorrect. While Vertex AI can store and serve trained models, this capability is not specific to Vertex ML Metadata.
- C. Correct.
Correct. Vertex ML Metadata provides visualization tools to explore the relationships between various components of your ML workflows.
- D. Incorrect.
Incorrect. Hyperparameter tuning is not a feature of Vertex ML Metadata; it is managed through Vertex AI Hyperparameter Tuning.
- E. Correct.
Correct. Vertex ML Metadata helps in auditing and debugging by maintaining a history of executions, their inputs, and outputs.