Practice Best Practices For Ml Pipelines (14.7) - Machine Learning Pipelines and Automation

Practice: Best Practices for ML Pipelines

Lesson 14.7Machine Learning Pipelines and Automation

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9 questions

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2 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 does it mean to have modular ML pipelines?
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Think about how interchangeable parts work.
Question 2.
Why is tracking important in ML pipelines?
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Consider the necessity of record-keeping in experiments.
3Question 3.
What is the primary benefit of keeping ML pipelines modular?
- Improved accuracy
- Easier maintenance
- Higher costs
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Think about how different parts of a machine can be replaced.
4Question 4.
Is it important to track everything in machine learning projects?
- True
- False
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Remember what happens when you lose track of your data!
Question 5.
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.
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Consider what areas in the pipeline could become bottlenecks.
Question 6.
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.
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What sort of thresholds might trigger human intervention?

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