MLS-C01 Question 176
Select 3An e-commerce company has deployed a machine learning model to recommend products to users. Over time, the performance of the recommendation model has degraded due to changes in user behavior. The company wants to update and retrain the model to improve its accuracy. Which of the following approaches would be most appropriate to ensure the updated model performs well and stays relevant?
- A
Use Amazon SageMaker Pipelines to automate the retraining and deployment process whenever new data is available.
- B
Manually retrain the model once a year, regardless of data patterns, to ensure consistency in updates.
- C
Implement Amazon SageMaker Model Monitor to detect data drift in real time and trigger retraining workflows.
- D
Retrain the model on the entire historical dataset, including the new data, to ensure the model learns from all available information.
- E
Use Amazon SageMaker Feature Store to track and serve versioned features for consistent model updates.
Show answer and explanation
Correct answers: A, C, E
Explanation
Ensuring that a machine learning model remains accurate and relevant involves automating the retraining process, monitoring for data drift, and managing feature consistency. Amazon SageMaker Pipelines enables efficient automation of retraining and deployment workflows, while SageMaker Model Monitor detects data drift to trigger retraining as needed. Additionally, SageMaker Feature Store ensures that versioned features are consistently managed for reliable updates. Together, these approaches provide a robust solution for updating and retraining models effectively.
- A. Correct.
Using Amazon SageMaker Pipelines to automate the retraining and deployment process ensures that updates are performed efficiently and accurately whenever new data is available. This is a scalable solution for maintaining the model's performance.
- B. Incorrect.
Manually retraining the model once a year is not ideal because it ignores changes in data patterns and user behavior, leading to potential degradation in model performance over time.
- C. Correct.
Amazon SageMaker Model Monitor is designed to track data drift and other metrics in real time. Detecting data drift can help identify when retraining is necessary, ensuring that the model remains relevant.
- D. Incorrect.
Retraining the model on the entire historical dataset can be computationally expensive and may introduce unnecessary noise. Incremental training or focusing on new data is often a better approach for maintaining model performance.
- E. Correct.
Amazon SageMaker Feature Store helps manage and serve versioned features consistently, which is crucial for retraining workflows. This ensures that updates are based on reliable and reproducible data.