Case context: A team tries one change to improve an image-classification model. The new version performs worse on validation data, and the engineers begin to think the idea was a dead end.

Question: What core lesson about machine learning development are they missing, and what should they do next?

Sample answer: They are missing that machine learning usually improves through repeated cycles of trying ideas, measuring results, and revising the plan. A weak first result does not mean the project has failed. Their next step should be to study what the experiment revealed, use that information to propose a new change, implement it, and run another test.

Key points:

• Progress in ML usually comes from repeated iterations.
• An early bad result is normal.
• The team should learn from the experiment instead of stopping.
• They should return to the idea-code-test loop.

Rubric: The answer should say that early failure is expected and that the team should use the outcome to design and test another idea.