Designing resilient fault-tolerant distributed training and batch-inference schedulers for dynamic vehicle fleets with unpredictable unplug events using Reed-Solomon tensor sharding.
Distributing tasks with Reed-Solomon parity fragments guarantees that calculations complete successfully even if 25% of vehicles depart mid-job.