Build, measure, learn
Good ML development is a tight loop: form a hypothesis, make one change, measure it on a fixed validation set, and decide. The faster and cleaner each lap, the faster you improve.
- Change one thing at a time so the effect is attributable.
- Keep the evaluation fixed so scores stay comparable.
- Log the result of every lap, including failures.
Speed matters more than cleverness
A team that runs ten honest experiments a day learns faster than one that runs a perfect experiment a week. Cheap fast feedback compounds.
- Use a small subset for quick smoke tests.
- Promote only promising changes to full runs.
- Cut the cycle time before chasing the next idea.
The cycle
Each lap should either confirm or kill a specific idea.
Key idea
ML progress comes from a fast disciplined loop of one change, one measurement, one decision on a fixed evaluation, where cycle speed compounds into faster learning.