Practice Deployment Scenarios (20.1.2) - Deployment and Monitoring of Machine Learning Models

Practice: Deployment Scenarios

Lesson 20.1.2Deployment 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 batch inference?
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Think about periodic processing of data.
Question 2.
Name a scenario where online inference is utilized.
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Consider instant feedback applications.
3Question 3.
What does batch inference involve?
- Real-time predictions
- Predictions on large datasets at intervals
- Only predictions on small datasets
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Think about how often predictions are made.
4Question 4.
Online inference is best for scenarios where?
- True
- False
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Consider applications that benefit from instant feedback.
Question 5.
Create a strategy for an online retail platform planning to transition from batch inference to online inference. Discuss potential challenges and solutions.
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Focus on balancing efficiency and user experience.
Question 6.
How would the deployment of an ML model for a self-driving car involve aspects of both edge and online inference? Discuss operational advantages.
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Consider the implications of decision-making speed for safety and efficiency.

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