14.7 - Best Practices for ML Pipelines
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Practice Questions
Test your understanding with targeted questions
What does it mean to have modular ML pipelines?
💡 Hint: Think about how interchangeable parts work.
Why is tracking important in ML pipelines?
💡 Hint: Consider the necessity of record-keeping in experiments.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What is the primary benefit of keeping ML pipelines modular?
💡 Hint: Think about how different parts of a machine can be replaced.
Is it important to track everything in machine learning projects?
💡 Hint: Remember what happens when you lose track of your data!
1 more question available
Challenge Problems
Push your limits with advanced challenges
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.
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?
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