Practice Deployment And Monitoring Of Machine Learning Models (20) - Deployment and Monitoring of Machine Learning Models

Practice: Deployment and Monitoring of Machine Learning Models

Lesson 20Deployment and Monitoring of Machine Learning Models

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9 questions

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4 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 deployment?
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Think about how models interact with real-time data.
Question 2.
Name one advantage of using containers for model deployment.
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Consider the benefits of separation.
3Question 3.
What does 'model deployment' refer to in machine learning?
- Integrating model for predictions
- Training a model
- Testing a model
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Think about the process of making a model usable in the field.
4Question 4.
True or False: Data drift can affect model performance.
- True
- False
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Consider how changing conditions can impact predictions.
Question 5.
You have deployed a model to predict customer churn, but its accuracy has dropped in recent months. Describe how you would identify the cause and address it.
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Reflect on how external factors could change customer behavior.
Question 6.
If you are running a batch inference system for an e-commerce website where customer behavior changes frequently, how would you modify the pipeline to enhance performance?
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Think about the timing and frequency of data updates.

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