Experiment tracking in the platform on the Build Your Own AI Platform track. The platform should attach metrics to the job and the version. People should not keep the best score in a chat message.
This lesson assumes you already worked through Labeling operations.
The idea in practice
Compare versions on the same dataset snapshot. Show the baseline next to the candidate.
A concrete check
goal = {
'track': 'Build Your Own AI Platform',
'lesson': 'Experiment tracking in the platform',
}
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 metric with no dataset id. 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 what a comparison view must show.
Self-check
- Say Experiment tracking in the platform 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.