Google Professional Machine Learning Engineer Question 411
Select 4Google Cloud PlatformYou are managing a machine learning model in production on Google Cloud. The model performance has started to degrade due to data drift. You want to automate the retraining process to maintain the model's performance. Which of the following steps should you include in your solution to automate model retraining effectively?
- A
Set up a data pipeline to monitor and log incoming data for drift detection.
- B
Implement a scheduled retraining process using Vertex AI Pipelines.
- C
Manually trigger retraining whenever you notice a performance drop.
- D
Use Cloud Functions to trigger the retraining pipeline when a drift threshold is exceeded.
- E
Store the retrained model versions in a centralized repository like Artifact Registry.
Show answer and explanation
Correct answers: A, B, D, E
Explanation
To automate model retraining effectively in Google Cloud, you need to set up a system that detects data drift, triggers retraining workflows based on drift thresholds, and stores updated models for deployment. Key components include monitoring data for drift, using tools like Vertex AI Pipelines for workflow automation, and leveraging services like Cloud Functions for event-driven triggers. Manual processes are excluded as they contradict automation goals.
- A. Correct.
Setting up a data pipeline to monitor and log incoming data for drift detection is essential for identifying when retraining is necessary. This step ensures that the system can detect data drift automatically.
- B. Correct.
Using Vertex AI Pipelines to implement a scheduled process allows you to automate the retraining workflow and integrate it with other Google Cloud services.
- C. Incorrect.
Manually triggering retraining is not an automated approach and contradicts the goal of setting up an automated retraining process.
- D. Correct.
Cloud Functions can be used to trigger retraining workflows automatically when drift thresholds are exceeded, providing a serverless and event-driven solution.
- E. Correct.
Storing retrained model versions in a centralized repository like Artifact Registry ensures version control and easy deployment of updated models.