Explain how the choice of data distributions for dev and test sets impacts team efficiency in machine learning application development.
Question: When developing a specific machine learning application, how does drawing the dev and test sets from the same distribution affect the team's efficiency compared to research-focused projects? Discuss the rationale behind this recommendation using concepts from Machine Learning Yearning.
Sample answer: In machine learning application development, drawing the dev and test sets from the same distribution is recommended because it makes the team more efficient. When dev and test sets share the same distribution, the team has a single, clear target to optimize for. In contrast, developing algorithms that train on one distribution and generalize to another is an important research problem, but attempting to solve this domain discrepancy during product development introduces complexity that slows down progress.
Key points:
- Choosing dev and test sets from the same distribution increases team efficiency.
- Research progress often focuses on developing algorithms that generalize from one distribution to another.
- For a specific application goal, avoiding different distributions for dev and test sets prevents team distraction and inefficiency.
Rubric: The response must explain that same-distribution dev and test sets improve team efficiency by providing a clear optimization target, and contrast this application-focused goal with research-focused goals that study generalization across different distributions.
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Choosing dev and test sets from the same distribution makes a team more efficient when building a specific ML application.
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Ng's recommendation to use same-distribution dev/test sets applies equally to both application progress and research progress goals.
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Match each recommendation or outcome to the correct project goal from Machine Learning Yearning.
Order the steps for distinguishing application vs. research goals when deciding on dev/test distributions.
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