Retraining later on the How to Train Models track. Retrain on a schedule or when data drifts. Compare the new model to the current one on a fresh window. Deploy only if it wins and does not fail a slice.
This lesson assumes you already worked through Artifacts you must save.
The idea in practice
Keep the old artifact. Roll back if the live metric drops.
A concrete check
goal = {
'track': 'How to Train Models',
'lesson': 'Retraining later',
}
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
Replacing production weights nightly with no comparison. 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 rule that blocks an automatic deploy.
Self-check
- Say Retraining later 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.