Data pipelines on the Build Your Own AI Platform track. Pipelines land raw data, validate it, and publish a snapshot id. Training consumes a snapshot, not 'whatever is in the table now'.
This lesson assumes you already worked through Features for training and serving.
The idea in practice
Fail the pipeline on schema changes you did not approve. Keep bad rows out with a quarantine.
A concrete check
goal = {
'track': 'Build Your Own AI Platform',
'lesson': 'Data pipelines',
}
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
Training on a live table that changes during the 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
Name the snapshot id and one validation that fails the pipeline.
Self-check
- Say Data pipelines 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.