Build versus buy on the Build Your Own AI Platform track. Buy a platform when your needs match a product and you can accept its limits. Build when tenancy, data control, or workflow is the product. Many teams buy inference and build only the registry and eval.
This lesson assumes you already worked through Incidents.
The idea in practice
Write the requirement that a vendor cannot meet before you build that piece.
A concrete check
goal = {
'track': 'Build Your Own AI Platform',
'lesson': 'Build versus buy',
}
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
Building a GPU scheduler because it sounded like a platform, with no models yet. 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
Mark build or buy for registry, inference, and labeling.
Self-check
- Say Build versus buy 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.