Practice Why Monitoring Is Crucial (20.4.1) - Deployment and Monitoring of Machine Learning Models

Practice: Why Monitoring is Crucial

Lesson 20.4.1Deployment and Monitoring of Machine Learning Models

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9 questions

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3 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
3 questions · about 2 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 changes in the inputs over time.
Question 2.
What do we monitor to keep our machine learning models effective?
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Consider the important factors that reflect model performance.
3Question 3.
What is the primary reason for monitoring machine learning models?
- To keep them updated
- To maintain accuracy and performance
- To reduce execution time
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Consider what monitoring keeps intact over time.
4Question 4.
True or False: Concept drift refers to changes in the input data distribution.
- True
- False
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Differentiate between input data changes and relationship changes.
Question 5.
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?
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Think about what critical indicators could indicate a need for action.
Question 6.
Design a study that tests the impact of data drift on model accuracy using historical data. Outline your methodology.
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Determine how to quantify changes in prediction accuracy against shifting data.

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