Google Professional Machine Learning Engineer Question 185
Select 3Google Cloud PlatformYou are building a machine learning model to predict customer churn for a subscription-based service. To ensure reproducibility and effective collaboration across your team, you need to track and organize your ML experiments on Google Cloud. Which of the following approaches should you adopt to effectively track and manage your experiments?
- A
Use Vertex AI Experiments to log metadata such as hyperparameters, metrics, and model artifacts for each experiment.
- B
Store model training scripts and experiment configurations in a version control system like Git.
- C
Manually record hyperparameter values, metrics, and results in a shared spreadsheet.
- D
Enable hyperparameter tuning jobs in Vertex AI to automatically track and compare results across experiments.
- E
Use Cloud Storage buckets to store model artifacts and manually track changes across iterations.
Show answer and explanation
Correct answers: A, B, D
Explanation
Tracking and running ML experiments effectively is crucial for reproducibility, collaboration, and optimization. Using managed tools like Vertex AI Experiments and hyperparameter tuning jobs simplifies experiment tracking and comparison. Additionally, version control systems like Git ensure that code and configurations are consistently managed and shared across teams. Manual methods, such as spreadsheets or manually tracking in Cloud Storage, are less reliable and scalable for managing ML experiments.
- A. Correct.
Correct: Vertex AI Experiments provides a managed way to log and track experiment metadata, making it easy to organize and analyze your experiments.
- B. Correct.
Correct: Storing training scripts and configurations in a version control system like Git ensures code reproducibility and allows team members to collaborate effectively.
- C. Incorrect.
Incorrect: Manually recording experiment data in a spreadsheet is error-prone and does not scale well for managing multiple experiments or ensuring reproducibility.
- D. Correct.
Correct: Vertex AI's hyperparameter tuning jobs not only optimize hyperparameters but also automatically log and compare results, aiding in experiment tracking.
- E. Incorrect.
Incorrect: While Cloud Storage is useful for storing artifacts, manually tracking changes across iterations is inefficient and prone to errors. Automated tools like Vertex AI Experiments are better suited for this purpose.