Practice Model Lifecycle Management (20.5.1) - Deployment 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 model retraining?
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Think about why models might need updates.
Question 2.
What is data drift?
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Consider how data can evolve over time.
3Question 3.
What is a reason for retraining a model?
- Data drift
- Model overtraining
- Static data
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Recall the factors that can affect model relevance.
4Question 4.
True or False: Automated pipelines eliminate the need for human intervention entirely.
- True
- False
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Consider the role of humans in monitoring models.
Question 5.
Design an automated retraining pipeline for a customer segmentation model, addressing potential challenges.
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Consider the different components involved in deploying and managing machine learning models.
Question 6.
Evaluate the impact of concept drift versus data drift on a fraud detection model.
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Focus on how changes in patterns can challenge a model's ability to function accurately.

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