Google Professional Machine Learning Engineer Question 188
Select 3Google Cloud PlatformYou are developing a machine learning model to predict customer churn and need to track and compare multiple training experiments to identify the best-performing model. You are using Google Cloud services for this task. Which of the following steps should you take to ensure reproducibility and effective tracking of your ML experiments?
- A
Use Vertex AI Experiments to log parameters, metrics, and artifacts for each training run.
- B
Manually save experiment details in a spreadsheet to track hyperparameters and results.
- C
Use a consistent naming convention for model versions and store them in Google Cloud Storage.
- D
Leverage TensorBoard to visualize training metrics and monitor experiment performance.
- E
Train models on separate local environments to ensure flexibility in experimentation.
Show answer and explanation
Correct answers: A, C, D
Explanation
To effectively track and run ML experiments, you should leverage tools like Vertex AI Experiments to log parameters, metrics, and artifacts systematically, use consistent naming conventions to manage model versions, and utilize TensorBoard for visualizations. These practices enhance reproducibility, scalability, and efficient comparison of experiments, which are critical in a cloud-based ML workflow.
- A. Correct.
Vertex AI Experiments is specifically designed to log and track experiment details, such as parameters, metrics, and artifacts, ensuring reproducibility and optimized model comparison.
- B. Incorrect.
Manually saving experiment details in a spreadsheet is error-prone and inefficient compared to automated tooling like Vertex AI Experiments.
- C. Correct.
Using consistent naming conventions for model versions and storing them in Google Cloud Storage helps organize and manage different versions of your model for reproducibility.
- D. Correct.
TensorBoard allows visualization of training metrics, which is essential for monitoring the performance of experiments and comparing results effectively.
- E. Incorrect.
Training models on separate local environments can lead to inconsistencies and lack of proper tracking, as it is harder to standardize and reproduce results.