The project loop on the AI Learning track. The loop is question, data audit, baseline, error analysis, one change, evaluate. One change at a time. Otherwise you will not know what helped.
This lesson assumes you already worked through How to study this track.
The idea in practice
Freeze the question. Make one change. Record the validation delta. Keep or revert.
A concrete check
goal = {
'track': 'AI Learning',
'lesson': 'The project loop',
}
checks = [
'input available at decision time',
'score matches the real decision',
'failure case written down',
]
print(goal['lesson'])
for item in checks:
print('-', item)
Run the sketch locally if you have Python. The printout is a reminder of the checks, not a trained model. Replace the strings with the real inputs from your own example before you treat it as a design.
What usually goes wrong
Changing features, model, and metric in the same run. When this happens, stop adding parameters or tools. Fix the check, the data, or the permission, then run the same example again.
What to write down
- The input you are allowed to use at decision time.
- The output and the score or pass rule.
- One failure you will test on purpose.
- What you will not claim the system can do.
Practice
Write the next change you would try after a baseline, and what result would make you revert it.
Self-check
- Say The project loop in one sentence that mentions an input and an output.
- Name the failure mode in this lesson and the check that would catch it.
Done when: you can explain this lesson without the page open, and you have a written failure case.