Practice Understanding Model Deployment (20.1) - Deployment and Monitoring of Machine Learning Models

Practice: Understanding Model Deployment

Lesson 20.1Deployment 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 model deployment in machine learning?
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Think about what happens after a model is trained.
Question 2.
Name the three deployment scenarios discussed.
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Consider the ways predictions can be made based on data availability.
3Question 3.
What is the process of integrating a machine learning model into production called?
- Training
- Monitoring
- Deployment
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Think about what happens after the model is 'trained'.
4Question 4.
True or False: Edge deployment can only happen on high-performance servers.
- True
- False
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Consider the limitations of devices in edge scenarios.
Question 5.
Design a deployment strategy for a new real-time traffic prediction model. What are the steps and considerations?
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Consider how data will be presented and interacted with by users.
Question 6.
Discuss potential issues that arise with batch inference and how to mitigate them.
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Think about the risks of using old data.

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