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Why faster development cycles improve machine learning results
Question: Explain how the length of a machine learning development cycle affects the pace of progress on a project.
Sample answer: When a team can move quickly from one round of data preparation, model training, evaluation, and error analysis to the next, it can test ideas more often and learn from mistakes sooner. As a result, shorter development cycles usually lead to faster overall progress on the machine learning task.
Key points:
- Progress depends on how quickly the team can complete each development cycle.
- Shorter cycles allow more experiments in the same amount of time.
- Faster feedback leads to faster improvement.
- The relationship between cycle time and progress is direct.
Rubric: The student should explain that machine learning progress speeds up when the iterative development cycle is completed more quickly. The answer must make clear that shorter cycle time leads to faster overall progress.
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