Specify a model you could train on the How to Make AI Models track. Write a spec: data shape, family, head, loss, parameter sketch, baseline, and latency budget. You do not have to train a giant model to have made a real design.
This lesson assumes you already worked through Interview review.
The idea in practice
The spec should be specific enough that someone else could implement the forward pass.
A concrete check
goal = {
'track': 'How to Make AI Models',
'lesson': 'Specify a model you could train',
}
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 paragraph that only names a product. 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
Fill the spec for one decision your site actually makes.
Self-check
- Say Specify a model you could train 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.