Treat compute as a scheduled resource
CPU, memory, GPU time and wall-clock duration are part of the job specification. Production ML benefits from the same discipline rather than assuming unlimited runtime resources.
Separate stages
Preprocessing, feature generation, model execution and report generation should be separable jobs with explicit inputs and outputs. This improves observability and restartability.
Design for partial failure
In a large batch, some tasks will fail. The system should record failure at the smallest useful unit and continue processing work that is independent.
Operational thinking matters
Scheduling systems force developers to think about queues, back-pressure and resource contention. Those ideas translate directly to large-scale AI inference and evaluation.