How does distributed training work in ML systems?

Distributed training splits model training across multiple GPUs or machines.

It uses data parallelism or model parallelism. Data parallelism replicates the model across nodes and splits data batches. Gradients are aggregated using all-reduce operations. This reduces training time significantly for large datasets.