Google Professional Machine Learning Engineer Question 186
Select 3Google Cloud PlatformYou are working as a machine learning engineer for a company building a predictive model to forecast product demand. Your team is running multiple experiments to fine-tune hyperparameters and evaluate different feature engineering techniques. You want to ensure that experiments are tracked properly and can be reproduced later if needed. Which of the following practices should you implement?
- A
Use a version-controlled system to track changes in data preprocessing and model code.
- B
Maintain a spreadsheet to manually log experiment results and hyperparameter configurations.
- C
Leverage tools like Vertex AI, MLflow, or TensorBoard to log and visualize experiment metrics.
- D
Store raw datasets and their transformations in a centralized, versioned data repository.
- E
Only keep track of successful experiments to reduce unnecessary clutter.
Show answer and explanation
Correct answers: A, C, D
Explanation
Tracking and running ML experiments involves adopting best practices that ensure reproducibility, transparency, and efficiency. Version control for code, automated experiment tracking tools, and a centralized repository for data transformations help maintain a clear record of changes, results, and configurations. These practices enable robust experiment management, allowing you to reproduce and analyze experiments effectively.
- A. Correct.
Using a version-controlled system for tracking changes in code ensures reproducibility and allows you to understand how specific code changes impact model performance.
- B. Incorrect.
Manually logging experiment results in a spreadsheet is error-prone and inefficient, especially for large-scale ML workflows, making it unsuitable for robust experiment tracking.
- C. Correct.
Experiment tracking tools like Vertex AI, MLflow, or TensorBoard provide automated logging, visualization, and comparison of experiment metrics, making them essential for managing ML experiments effectively.
- D. Correct.
Storing raw datasets and their transformations in a versioned repository ensures data lineage and reproducibility, which are critical for understanding and re-running experiments.
- E. Incorrect.
Only tracking successful experiments limits your ability to learn from failed ones and hinders reproducibility, making this a poor practice for effective ML experimentation.