Which of the following statements BEST describes the difference between data drift and model drift, and how you would address them using Amazon SageMaker?

You are a machine learning engineer at an e-commerce company that uses a recommendation model to suggest products to customers. The model was trained on data from the past year, but after being in production for several months, you notice that the model’s recommendations are becoming less relevant. You suspect that either data drift or model drift could be causing the decline in performance. To investigate and resolve the issue, you need to understand the difference between these two types of drift and how to monitor them using Amazon SageMaker.

Which of the following statements BEST describes the difference between data drift and model drift, and how you would address them using Amazon SageMaker?
A . Data drift occurs when the distribution of the input data changes over time, while model drift happens when the model’s underlying assumptions or parameters become outdated. To address data drift, you should use SageMaker Model Monitor to track changes in input data distribution. For model drift, you should periodically retrain the model using the latest data
B . Data drift is a sudden change in the model’s accuracy, while model drift is a gradual degradation in model performance. You should use SageMaker Feature Store to manage both types of drift by standardizing input data
C . Data drift occurs when the model’s predictions start to deviate from the expected outcomes, while model drift occurs when the model’s accuracy declines due to changes in the input data. SageMaker Pipelines should be used to automate retraining for both types of drift
D . Data drift refers to changes in the model’s accuracy due to shifts in the data, while model drift refers to changes in the underlying data features over time. To address both, you should use SageMaker
Clarify to detect bias and retrain the model monthly

View Answer

Answer: A

Explanation:

Correct option:

Data drift occurs when the distribution of the input data changes over time, while model drift happens when the model’s underlying assumptions or parameters become outdated. To address data drift, you should use SageMaker Model Monitor to track changes in input data distribution. For model drift, you should periodically retrain the model using the latest data

This option correctly defines data drift as changes in the distribution of the input data over time, which can lead to the model receiving data that is different from what it was trained on.

Model drift, on the other hand, occurs when the model’s performance degrades because its assumptions or parameters no longer align with the real-world data. SageMaker Model Monitor can be used to detect data drift by tracking changes in data distribution, while model drift is addressed by retraining the model with updated data.

via – https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html

Incorrect options:

Data drift refers to changes in the model’s accuracy due to shifts in the data, while model drift refers to changes in the underlying data features over time. To address both, you should use SageMaker Clarify to detect bias and retrain the model monthly – This option incorrectly describes data drift as related to model accuracy and model drift as related to changes in data features. SageMaker Clarify is used for bias detection, not specifically for drift detection.

Data drift occurs when the model’s predictions start to deviate from the expected outcomes, while model drift occurs when the model’s accuracy declines due to changes in the input data. SageMaker Pipelines should be used to automate retraining for both types of drift – This option incorrectly defines data drift and model drift. Data drift is about changes in data distribution, not prediction deviations. SageMaker Pipelines is for automating ML workflows, not directly for drift detection.

Data drift is a sudden change in the model’s accuracy, while model drift is a gradual degradation in model performance. You should use SageMaker Feature Store to manage both types of drift by standardizing input data – This option misinterprets the nature of data and model drift. SageMaker Feature Store is used for managing and serving features, not for directly addressing drift.

Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html