Google Professional Machine Learning Engineer Question 413
Select 3Google Cloud PlatformYou are managing a machine learning model deployed in production on Google Cloud. The model's performance is monitored using Cloud Monitoring, and you have implemented an automated retraining pipeline using Vertex AI Pipelines. Recently, you noticed a decrease in the model's accuracy due to data drift. What are the MOST important considerations when automating the model retraining process to address the data drift?
- A
Ensure that the data validation step is included to detect anomalies in the new training data before retraining.
- B
Set up a trigger to retrain the model every fixed interval, such as weekly, regardless of data quality or model performance.
- C
Incorporate feature engineering and preprocessing steps in the retraining pipeline to maintain consistency with the original training pipeline.
- D
Automatically deploy the newly retrained model to production without any human review to reduce downtime.
- E
Use a performance monitoring metric, such as prediction accuracy or F1 score, to determine when retraining needs to be triggered.
Show answer and explanation
Correct answers: A, C, E
Explanation
To automate model retraining effectively, it is crucial to monitor data and model performance continuously. The retraining process should include essential steps such as data validation and consistent preprocessing to ensure data quality and alignment with the original pipeline. Additionally, triggering retraining based on performance metrics ensures that retraining occurs only when necessary, optimizing resource usage and maintaining model accuracy in production.
- A. Correct.
Including a data validation step is essential to ensure the new training data is clean, consistent, and free of anomalies before retraining the model. Skipping this step could lead to poor model performance.
- B. Incorrect.
Retraining the model at fixed intervals without considering data quality or performance metrics may lead to unnecessary retraining or retraining with unrepresentative data.
- C. Correct.
Incorporating feature engineering and preprocessing steps ensures that the retraining pipeline is consistent with the original training pipeline, avoiding mismatches that could degrade model performance.
- D. Incorrect.
Automatically deploying the retrained model without human review is risky, as it may lead to deployment of a poorly performing model. Proper model validation steps should be in place before deployment.
- E. Correct.
Using performance metrics like accuracy or F1 score to trigger retraining ensures that retraining is only initiated when the model's performance drops below an acceptable threshold, making the process more efficient.