Distributed Training
Distributed training is a machine learning technique that splits the training process across multiple computing devices (e.g., GPUs, TPUs, or servers) to handle large datasets or complex models more efficiently. It enables parallel processing by distributing data, model parameters, or both, significantly reducing training time and allowing for scalability beyond single-machine limitations. This approach is essential for training state-of-the-art deep learning models that require immense computational resources.
Developers should learn distributed training when working with large-scale machine learning projects, such as training deep neural networks on massive datasets (e.g., in natural language processing or computer vision), where single-device training is too slow or infeasible. It is crucial for applications in industries like autonomous vehicles, healthcare AI, or recommendation systems, where model accuracy and speed are critical. Using distributed training can cut training times from weeks to days or hours, enabling faster experimentation and deployment.