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Consequence of randomly choosing between improving search or scoring during inference debugging
Question: According to Machine Learning Yearning, why is it problematic for researchers to randomly decide whether to work on the search algorithm or the scoring function when debugging an inference failure?
Sample answer: Randomly choosing which component to improve is problematic because the two causes of failure (search algorithm vs. scoring function) require completely different engineering responses. Working on the wrong component will not resolve the underlying failure, resulting in wasted development effort.
Key points:
- The two possibilities of failure require prioritizing efforts very differently (improving search vs. improving the learning algorithm).
- Randomly choosing can lead to working on the wrong component, which will not fix the actual bug.
Rubric: The answer should explain that the two failure types require completely different priorities/actions, and randomly choosing can lead to prioritizing the wrong algorithm, wasting effort without fixing the issue.
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References
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Tags
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
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Consequence of randomly choosing between improving search or scoring during inference debugging