MLA-C01 Question 359
Select 3You have deployed a machine learning model to production on Amazon SageMaker. After a few weeks of monitoring, you notice that the model's performance has degraded due to a change in the input data distribution. Which actions should you take to address this issue and maintain the model's performance?
- A
Retrain the model with new data that reflects the updated input distribution.
- B
Use Amazon SageMaker Model Monitor to automatically correct the predictions based on the new data.
- C
Perform data preprocessing or feature engineering to align the input data with the model's expectations.
- D
Update the model's hyperparameters without retraining to adapt to the new data distribution.
- E
Implement a data drift detection mechanism using Amazon SageMaker Model Monitor to alert when input data distribution changes.
Show answer and explanation
Correct answers: A, C, E
Explanation
When a model's performance degrades due to changes in input data distribution, it is essential to take corrective actions. Retraining the model with updated data ensures it learns the new distribution. Adjusting data preprocessing can also help in aligning the data with the model's needs. Additionally, setting up a data drift detection mechanism with Amazon SageMaker Model Monitor allows for proactive identification of future data distribution changes, enabling timely remediation.
- A. Correct.
Retraining the model with new data that reflects the updated input distribution is a fundamental step to address performance degradation caused by data drift.
- B. Incorrect.
Amazon SageMaker Model Monitor cannot automatically correct predictions. It is a tool for detecting issues such as data drift, but it does not modify model predictions.
- C. Correct.
Adjusting data preprocessing or feature engineering can help align the input data with the model's expectations, mitigating performance degradation.
- D. Incorrect.
Updating hyperparameters without retraining the model is not a valid approach. Hyperparameters are optimized during training and cannot adapt a model to new data distributions without retraining.
- E. Correct.
Implementing a data drift detection mechanism using Amazon SageMaker Model Monitor is a proactive step to identify future instances of input data distribution changes.