Google Professional Machine Learning Engineer Question 485
Select 3Google Cloud PlatformYou have deployed a machine learning model on Google Cloud AI Platform to predict customer churn. After a few weeks, you notice a significant drop in model performance. Upon investigation, you suspect data drift might be the cause. How should you monitor and address this issue using Google Cloud tools?
- A
Set up continuous monitoring with Vertex AI Model Monitoring to track feature distribution and label drift.
- B
Periodically retrain your model using the same historical dataset to ensure consistency.
- C
Enable Vertex AI Explainable AI to analyze feature importance and identify changes in feature behavior.
- D
Set up alerts in Cloud Monitoring to notify you when prediction latency increases.
- E
Implement automated retraining pipelines in Vertex AI Pipelines to retrain the model when data drift exceeds a threshold.
Show answer and explanation
Correct answers: A, C, E
Explanation
To address data drift, leveraging Vertex AI Model Monitoring is crucial for tracking changes in input feature distribution and label drift. Explainable AI provides insights into how feature importance might have changed, which could signal drift. Additionally, implementing automated retraining pipelines ensures that the model adapts to new data distributions efficiently. Simply retraining on historical data or monitoring unrelated metrics like latency will not effectively mitigate data drift.
- A. Correct.
Correct. Vertex AI Model Monitoring is specifically designed to track data drift, feature distribution, and label drift, making it an essential tool for monitoring model performance over time.
- B. Incorrect.
Incorrect. Retraining with the same historical dataset will not address data drift because the dataset does not reflect the changing data distribution in production.
- C. Correct.
Correct. Vertex AI Explainable AI can help you understand how feature importance changes over time and whether there are unexpected shifts in feature behavior.
- D. Incorrect.
Incorrect. While Cloud Monitoring can track system metrics like latency, it does not directly address or monitor data drift in the model's input features or predictions.
- E. Correct.
Correct. Automated retraining pipelines in Vertex AI Pipelines can help address data drift by retraining the model with updated datasets when drift is detected, ensuring the model stays relevant.