Practice Best Practices And Challenges (20.6) - Deployment and Monitoring of Machine Learning Models

Practice: Best Practices and Challenges

Lesson 20.6Deployment and Monitoring of Machine Learning Models

Practise this section

10 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.
Sign up to start the exercise
Quiz
4 questions · about 3 min · pass mark 70%. Hints available.
Sign up to start the quiz

6 sample questions

Question 1.
What is the purpose of version control in machine learning?
Show hintHide hint
Think about why it’s important to go back to a previous state.
Question 2.
Define what is meant by reproducibility in the context of machine learning.
Show hintHide hint
Consider if someone else could get the same results.
3Question 3.
What is the key benefit of using version control in model deployment?
- Easier collaboration
- Ensures data security
- Tracks changes
Show hintHide hint
Think about why you would need to go back to an earlier state of your model.
4Question 4.
True or False: Continuous monitoring can help identify data drift.
- True
- False
Show hintHide hint
Consider what happens when the input data changes.
Question 5.
You are deploying a model that begins to show different performance metrics after a few months. What steps would you take to investigate potential data drift?
Show hintHide hint
Think about how you can track changes over time.
Question 6.
Describe a strategy to ensure model fairness and mitigate bias during the deployment process.
Show hintHide hint
Consider collaboration with ethicists and diverse stakeholders.

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