Your question is Race Conditions in ML Pipelines. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You're working on ML training and data pipelines where multiple distributed workers, schedulers, and retry paths can touch the same artifacts or state. You want a clean approach for handling concurrent updates without corrupting checkpoints, duplicating work, or producing inconsistent training outputs.
How do you handle race conditions in a distributed ML training environment?