Promotion is a release on the Build Your Own AI Platform track. Promotion moves a version from staging to production. It is a release with an owner, a report, and a time. It is not a file copy someone does from a laptop.
This lesson assumes you already worked through An evaluation service.
The idea in practice
Require the evaluation report, a rollback target, and a named owner. Write the promotion to an audit log.
A concrete check
goal = {
'track': 'Build Your Own AI Platform',
'lesson': 'Promotion is a release',
}
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
Pointing production at whatever finished training last. 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
List the three approvals you want before a version serves live traffic.
Self-check
- Say Promotion is a release 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.