Practice Common Challenges - 20.6.2 | 20. Deployment and Monitoring of Machine Learning Models | Data Science Advance
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is reproducibility in the context of machine learning?

💡 Hint: Think about testing and production environments.

Question 2

Easy

Why is data drift a concern in deployed machine learning models?

💡 Hint: Consider how data changes over time.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is the primary concern with data drift?

  • It causes models to become biased.
  • It affects the model's predictive power due to changing data distributions.
  • It leads to faster processing speeds.

💡 Hint: Think about how data changes after a model is initially trained.

Question 2

Load balancing improves system performance. True or False?

  • True
  • False

💡 Hint: Consider what happens when all requests go to a single server.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design an end-to-end pipeline that handles model deployment and retraining in response to data drift. Make sure to include all necessary components.

💡 Hint: Think about the flow of data and feedback mechanisms.

Question 2

A deployed model is showing signs of degradation in performance after a new feature was added. Discuss how you would analyze and rectify this issue.

💡 Hint: Consider steps in the model validation process.

Challenge and get performance evaluation