What is distributed training?

Distributed training is a machine learning technique where the training process is split across multiple machines or devices. This allows for faster training times, especially for large datasets and complex models that would be impractical to train on a single machine. It's commonly used to accelerate deep learning tasks and handle massive datasets by distributing the computational load.