Distributed Training

Distributed training is a machine learning technique that involves training a model across multiple computing devices or nodes. This approach allows for the parallel processing of data, significantly reducing the time required to train large models. Key characteristics include scalability, improved performance, and efficient use of resources, making it particularly useful for deep learning tasks that require substantial computational power. Common use cases include training neural networks on large datasets, such as image recognition or natural language processing tasks, where the volume of data can be overwhelming for a single machine.