Queues and workers on the Build Your Own AI Platform track. Training and batch inference are queue work. Workers pull jobs, heartbeat, and ack only after artifacts are saved. A crash retries safely.
This lesson assumes you already worked through Scheduling GPUs.
The idea in practice
Make the job idempotent. Poison messages go to a dead-letter queue a person can see.
A concrete check
goal = {
'track': 'Build Your Own AI Platform',
'lesson': 'Queues and workers',
}
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
Ack before the artifact is written, then losing the work. 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 when the worker is allowed to ack.
Self-check
- Say Queues and workers 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.