Size and latency on the How to Make AI Models track. Parameter count, memory, and milliseconds per example decide if a model can run where you need it. Measure on the target hardware.
This lesson assumes you already worked through Model cards.
The idea in practice
Benchmark one batch of the production size. Include preprocessing time.
A concrete check
goal = {
'track': 'How to Make AI Models',
'lesson': 'Size and latency',
}
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 latency number from a warm GPU that is not your server. 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 hardware you would measure and the batch size.
Self-check
- Say Size and latency 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.