Practice What To Monitor (20.4.2) - Deployment and Monitoring of Machine Learning Models

Practice: What to Monitor

Lesson 20.4.2Deployment and Monitoring of Machine Learning Models

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10 questions

Flashcard drill
5 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
4 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is data drift?
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Think about how new data can differ from the training data.
Question 2.
Name one performance metric you should monitor.
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Consider how we measure model correctness.
3Question 3.
What is the purpose of monitoring predictions?
- To enhance data input
- To assess and ensure accuracy
- To limit model exposure
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Consider why tracking model outputs is vital.
4Question 4.
True or False: Continuous monitoring can detect data drift.
- True
- False
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Think about how data patterns can change.
Question 5.
Design a monitoring plan for a newly deployed model that must operate in a dynamic environment. What factors will you include in your plan?
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Consider all elements discussed about monitoring.
Question 6.
Analyze the potential challenges of monitoring model performance in a high-volume application. What strategies can mitigate these challenges?
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Think about the scale and ways to manage data effectively.

6 more questions in this section. Enroll free to practise them all.