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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is data drift?
💡 Hint: Think about changes in the inputs over time.
Question 2
Easy
What do we monitor to keep our machine learning models effective?
💡 Hint: Consider the important factors that reflect model performance.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is the primary reason for monitoring machine learning models?
- To keep them updated
- To maintain accuracy and performance
- To reduce execution time
💡 Hint: Consider what monitoring keeps intact over time.
Question 2
True or False: Concept drift refers to changes in the input data distribution.
- True
- False
💡 Hint: Differentiate between input data changes and relationship changes.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Develop a plan for implementing an automated monitoring system for a deployed ML model. What metrics would you include, and how would you respond to alerts?
💡 Hint: Think about what critical indicators could indicate a need for action.
Question 2
Design a study that tests the impact of data drift on model accuracy using historical data. Outline your methodology.
💡 Hint: Determine how to quantify changes in prediction accuracy against shifting data.
Challenge and get performance evaluation