Practice Monitoring And Updating (1.6) - Chapter 6: AI and Machine Learning in IoT

Practice: Monitoring and Updating

Lesson 1.6AI and Machine Learning in IoT

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8 questions

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2 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
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6 sample questions

Question 1.
What is concept drift in machine learning?
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Think about why a model might fail over time.
Question 2.
Why is continuous monitoring important?
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Consider how environments change.
3Question 3.
What is the main reason for continuous monitoring of an ML model?
- To enhance theoretical understanding
- To maintain model accuracy over time
- To prepare for deployment
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Think about why models might fail.
4Question 4.
True or False: Concept drift means a model always improves its accuracy over time.
- True
- False
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Consider how models might lose touch with reality.
Question 5.
Design a continuous monitoring system for a remote environmental sensor that predicts air quality levels. Describe the components involved and how you would detect concept drift.
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Incorporate elements like user interface and feedback data from real-world applications.
Question 6.
Evaluate the impact of not addressing concept drift in a financial fraud detection system. What could be the consequences?
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Think about how outdated models may misinterpret new fraudulent patterns.

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