Service levels on the Build Your Own AI Platform track. An SLA says latency, availability, and what happens when you miss them. Models fail differently than ordinary APIs because quality can drop while HTTP 200 continues.
This lesson assumes you already worked through Dev, staging, production.
The idea in practice
Alert on quality samples, not only uptime. Define a degraded mode that returns a safe default.
A concrete check
goal = {
'track': 'Build Your Own AI Platform',
'lesson': 'Service levels',
}
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
An uptime SLO that stays green while answers become nonsense. 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 one latency target and one quality check.
Self-check
- Say Service levels 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.