Labeling operations on the Build Your Own AI Platform track. Labeling needs guidelines, agreement checks, and a queue. The platform stores who labeled what and which guide version they saw.
This lesson assumes you already worked through Data pipelines.
The idea in practice
Sample for agreement. Retrain guidelines when agreement drops. Do not silently relabel history.
A concrete check
goal = {
'track': 'Build Your Own AI Platform',
'lesson': 'Labeling operations',
}
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
One unlabeled spreadsheet and no record of the rule. 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 fields stored per label: who, when, guide version, value.
Self-check
- Say Labeling operations 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.