The practice of training machine learning models across multiple computing devices or clusters, rather than on a single machine, involves various architectural approaches and algorithmic adaptations. For instance, one approach distributes the data across multiple workers, each training a local model on a subset. These local models are then aggregated to create a globally improved model. This allows for the training of much larger models on much larger datasets than would be feasible on a single machine.
This decentralized approach offers significant advantages by enabling the processing of massive datasets, accelerating training times, and improving model accuracy. Historically, limitations in computational resources confined model training to individual machines. However, the exponential growth of data and model complexity has driven the need for scalable solutions. Distributed computing provides this scalability, paving the way for advancements in areas such as natural language processing, computer vision, and recommendation systems.
