Practice Best Practices (20.6.1) - Deployment and Monitoring of Machine Learning Models

Practice: Best Practices

Lesson 20.6.1Deployment and Monitoring of Machine Learning Models

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9 questions

Flashcard drill
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 version control?
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Think about how you can track changes like in documents.
Question 2.
Name one tool used for reproducible pipelines.
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Consider tools specifically designed for machine learning.
3Question 3.
What is the primary purpose of version control?
- To manage project budgets
- To track changes in models
- To analyze data trends
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Think about how changes need to be documented in data science.
4Question 4.
True or False: Reproducible pipelines ensure that every experiment is unique.
- True
- False
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Consider the meaning of reproducibility.
Question 5.
Consider a scenario where a deployed model starts showing poor performance. Outline your steps for troubleshooting and monitoring.
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Think about how changes in the data might affect model outcomes.
Question 6.
You have a team of data scientists who are experiencing difficulties collaborating on model development without version control. Propose a solution and describe its implementation.
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Consider how collaboration can be structured and documented.

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