Google Professional Machine Learning Engineer Question 444
Select 4Google Cloud PlatformYour team is building a machine learning model on Google Cloud for predicting product demand. To ensure reproducibility and traceability, you decide to implement model and dataset versioning. Which of the following steps should you take to properly integrate model and dataset versioning into your workflow?
- A
Use Vertex AI Model Registry to track and version trained models.
- B
Store datasets in Cloud Storage buckets with versioning enabled.
- C
Manually log dataset and model metadata in a spreadsheet for tracking.
- D
Integrate Vertex AI Experiments to track model hyperparameters and performance.
- E
Use Cloud Source Repositories for storing and versioning training scripts and configurations.
Show answer and explanation
Correct answers: A, B, D, E
Explanation
Proper integration of model and dataset versioning is crucial for reproducibility and traceability in machine learning workflows. Google Cloud provides tools like Vertex AI Model Registry for managing models, Cloud Storage versioning for tracking dataset changes, Vertex AI Experiments for monitoring experiment metadata, and Cloud Source Repositories for storing and versioning code and configurations. Avoid manual methods like spreadsheets, which are prone to errors and not scalable.
- A. Correct.
Correct: Vertex AI Model Registry is specifically designed for managing and versioning trained models, making it an essential tool for model versioning.
- B. Correct.
Correct: Enabling versioning on Cloud Storage buckets ensures that any modifications to the dataset are tracked, allowing for reproducibility of experiments.
- C. Incorrect.
Incorrect: Manually logging metadata in a spreadsheet is error-prone and not scalable. Automated tools like Vertex AI and Cloud Storage versioning are better suited for this purpose.
- D. Correct.
Correct: Vertex AI Experiments allows for tracking model hyperparameters, performance metrics, and other metadata, which is critical for ensuring reproducibility and traceability.
- E. Correct.
Correct: Cloud Source Repositories can be used for storing and versioning training scripts and configurations, ensuring that changes to the training process are tracked and reproducible.