In today's era of large-scale machine learning models, training on a single machine often becomes impractical due to resource constraints and time limitations. Distributed training provides an efficient solution by leveraging multiple computing resources to accelerate model training and handle larger datasets. This talk explores various approaches to distributed training, including data and model parallelism, synchronous and asynchronous strategies, using frameworks like TensorFlow and PyTorch.