Training jobs on the Build Your Own AI Platform track. A training job is a queued run with a data snapshot, code version, and resource limit. It writes a new version or it fails cleanly. It does not edit production.
This lesson assumes you already worked through Artifact storage.
The idea in practice
Capture logs and exit codes. Kill a job that exceeds time or spend.
A concrete check
goal = {
'track': 'Build Your Own AI Platform',
'lesson': 'Training jobs',
}
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
A notebook on a shared machine as the only trainer. 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 job spec fields: data id, code id, budget.
Self-check
- Say Training jobs 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.