Practice What Is Deployment? (20.1.1) - Deployment and Monitoring of Machine Learning Models

Practice: What is Deployment?

Lesson 20.1.1Deployment 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 what happens after training a model.
Question 2.
Name a type of inference that processes data in real time.
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This is different from batch processing.
3Question 3.
What is model deployment?
- A process of training models
- Integrating models into production
- Storing models in a database
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Think about what happens after a model is trained.
4Question 4.
True or False: Online inference processes data in batches at scheduled times.
- True
- False
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Recall the definition of online inference.
Question 5.
Design a deployment strategy for a new machine learning model that predicts customer churn in a retail application. Consider aspects like data flow, user access, and monitoring.
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Think about each step in terms of production use to ensure system effectiveness.
Question 6.
Evaluate the pros and cons of online vs. batch inference in the context of a healthcare application that analyzes patient data.
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Consider the application needs against data processing limitations.

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