Question: In a concise essay, explain why, according to Machine Learning Yearning, a few changes in prioritization can have a huge effect on a machine learning team's productivity.

Sample answer: A machine learning team's time and effort are limited resources, and much of the work in ML projects (like debugging, tuning, or collecting data) can be spent on paths that do not meaningfully improve results. Because of this, even a small shift in what the team chooses to focus on first can redirect effort away from low-impact activities toward high-impact ones, producing an outsized effect on overall productivity relative to the size of the change itself.

Key points:

• Team time and effort are limited resources
• Effort can be misallocated toward low-impact activities
• Even a few prioritization changes can redirect effort toward high-impact work
• This redirection produces a disproportionately large effect on productivity
• Directly grounded in the source claim from p. 7

Rubric: Full credit: response explains that limited team resources mean small shifts in focus can redirect effort meaningfully; connects this to the source claim about huge effects from a few changes; is coherent and grounded in the source. Partial credit: response restates the claim without explaining the underlying reasoning. No credit: response is off-topic or contradicts the source.