Evaluating an architecture choice on the How to Make AI Models track. An architecture wins only if it beats a simpler one on the same split, data, and budget. Otherwise the gain is a confound.
This lesson assumes you already worked through Quantization preview.
The idea in practice
Hold data and training budget fixed. Change only the architecture. Repeat with two seeds if the gap is small.
A concrete check
goal = {
'track': 'How to Make AI Models',
'lesson': 'Evaluating an architecture choice',
}
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 new architecture with a different split and a different number of epochs. 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 controls you would freeze in that comparison.
Self-check
- Say Evaluating an architecture choice 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.