Google Professional Machine Learning Engineer Question 507
Select 4Google Cloud PlatformYou are managing a machine learning model deployed to predict customer churn for a subscription service. After deployment, you observe a decline in model performance over time. To ensure the model remains effective, you decide to monitor its performance against baselines, a simpler logistic regression model, and its performance trends over time. Which of the following steps should you take to address the issue?
- A
Set up continuous evaluation pipelines to compare the model's predictions against ground truth labels as they become available.
- B
Track key performance metrics like accuracy, precision, and recall over time to identify performance drift.
- C
Replace the deployed model immediately with the simpler logistic regression model to eliminate potential issues.
- D
Monitor data distributions and input features for shifts that could be causing the performance decline.
- E
Compare the performance of the model against its historical performance to identify anomalies.
Show answer and explanation
Correct answers: A, B, D, E
Explanation
To effectively monitor and address model performance issues over time, it is essential to implement a combination of strategies including continuous evaluation, tracking key performance metrics, monitoring data distributions, and comparing historical performance. These steps provide deeper insights into the root cause of performance degradation and ensure the model remains effective. Simply replacing the model without investigation is not a best practice in machine learning operations.
- A. Correct.
Continuous evaluation pipelines are crucial for monitoring how the model performs on fresh data and identifying performance degradation over time. This is a critical step in managing model performance.
- B. Correct.
Tracking performance metrics like accuracy, precision, and recall over time helps you detect issues such as model drift or reduced effectiveness in predictions.
- C. Incorrect.
Replacing the model without proper investigation is not recommended, as it does not address the underlying cause of the performance decline. Simpler models are useful for benchmarking but not as a reactive solution.
- D. Correct.
Monitoring data distributions and feature shifts can help identify if the input data has changed significantly, which is a common cause of model performance degradation.
- E. Correct.
Comparing the model's current performance to its historical performance can help identify trends or anomalies, providing insights into why the decline is occurring.