Google Professional Machine Learning Engineer Question 184
Select 3Google Cloud PlatformYou are building a machine learning model to predict customer churn. You want to set up an experiment tracking system using Google Cloud tools to analyze the impact of different hyperparameters and preprocessing techniques. Which of the following steps are necessary to effectively track and run your ML experiments?
- A
Use Vertex AI Experiments to log parameters, metrics, and artifacts for each run.
- B
Store experiment data in BigQuery for advanced querying and visualization.
- C
Manually track hyperparameters and metrics in a spreadsheet for simplicity.
- D
Assign unique names and IDs to each experiment run to differentiate results.
- E
Use Cloud Monitoring to track GPU utilization during experiment training.
Show answer and explanation
Correct answers: A, B, D
Explanation
To effectively track and run ML experiments, it is essential to use tools like Vertex AI Experiments for logging and tracking, store data in scalable and queryable solutions like BigQuery, and ensure each run is uniquely identifiable. These practices ensure reproducibility, scalability, and efficient analysis of experiment outcomes. Cloud Monitoring, though valuable for monitoring compute resource utilization, is not directly tied to experiment tracking.
- A. Correct.
Vertex AI Experiments is a purpose-built tool for tracking ML experiments, enabling you to log parameters, metrics, and artifacts automatically. This is essential for reproducibility and tracking.
- B. Correct.
Storing experiment data in BigQuery allows for advanced querying and integration with visualization tools like Looker Studio (formerly Data Studio), providing deeper insights into experiment results.
- C. Incorrect.
Manually tracking experiments in a spreadsheet is error-prone and does not scale well for complex ML workflows. It is not a recommended practice in professional environments.
- D. Correct.
Assigning unique names and IDs to each experiment run is critical for managing and differentiating results, especially when running multiple experiments simultaneously.
- E. Incorrect.
While Cloud Monitoring is useful for resource monitoring, it is not directly related to tracking hyperparameters, metrics, or artifacts for experiments.