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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does it mean to have modular ML pipelines?
💡 Hint: Think about how interchangeable parts work.
Question 2
Easy
Why is tracking important in ML pipelines?
💡 Hint: Consider the necessity of record-keeping in experiments.
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 benefit of keeping ML pipelines modular?
- Improved accuracy
- Easier maintenance
- Higher costs
💡 Hint: Think about how different parts of a machine can be replaced.
Question 2
Is it important to track everything in machine learning projects?
- True
- False
💡 Hint: Remember what happens when you lose track of your data!
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Your team has implemented an ML pipeline that struggles with increasing user data. Describe in detail how you would modify the pipeline for scalability and what tools could facilitate this.
💡 Hint: Consider what areas in the pipeline could become bottlenecks.
Question 2
Imagine the accuracy of your model suddenly drops due to unforeseen circumstances. Explain how a human-in-the-loop mechanism could be employed to address this issue.
💡 Hint: What sort of thresholds might trigger human intervention?
Challenge and get performance evaluation