Databricks Machine Learning Professional Question 239
Select 3You are responsible for deploying a machine learning model for churn prediction at a subscription-based company. After deployment, you notice that the model's performance on new data has started to degrade. You suspect concept drift or data drift. Which of the following actions should you take to monitor and address drift in your model?
- A
Set up a drift detection mechanism to compare the statistical distribution of incoming data with the training data.
- B
Use a feature importance analysis to identify which features contribute the most to the model's predictions.
- C
Monitor the model's performance using business metrics, such as churn rate predictions, and compare them to actual outcomes.
- D
Re-train the model immediately on the new incoming data without analyzing the nature of the drift.
- E
Implement alerts for significant deviations in input data distributions or model predictions over time.
Show answer and explanation
Correct answers: A, C, E
Explanation
Detecting and monitoring drift is critical for maintaining machine learning model performance in production. Data drift occurs when the statistical properties of the input data change, while concept drift refers to changes in the relationship between features and the target variable. Actions such as setting up drift detection mechanisms, monitoring business metrics related to the model's predictions, and implementing alerts for data distribution changes are effective ways to address these issues. However, re-training the model without analyzing the drift may lead to problems, and feature importance analysis is not directly tied to drift monitoring.
- A. Correct.
Setting up a drift detection mechanism is a key practice to monitor changes in the statistical distribution of incoming data compared to the training data, which can help identify data drift.
- B. Incorrect.
Feature importance analysis is not directly related to detecting or monitoring drift but is useful for understanding the model's behavior.
- C. Correct.
Monitoring the model's performance using business metrics and comparing them to actual outcomes is essential for detecting concept drift, as it reflects changes in the relationship between input features and the target variable.
- D. Incorrect.
Re-training the model immediately on new data without understanding the drift can lead to suboptimal results or overfitting, as the nature of the drift and its impact on the model needs to be analyzed first.
- E. Correct.
Implementing alerts for significant deviations in input data distributions or model predictions over time helps in proactively catching drift issues and allows for timely intervention.