Learn how to efficiently scale machine learning model training across multiple GPUs and machines using TensorFlow's distribution strategies in this 35-minute Google I/O '18 conference talk. Explore the Distribution Strategy API, which enables distributed training with minimal code changes. Discover techniques for data parallelism, synchronous and asynchronous parameter updates, and model parallelism. Follow a demonstration of setting up distributed training on Google Cloud and examine performance benchmarks for ResNet50. Gain insights into optimizing input pipelines, including parallelizing file reading and transformations, pipelining with prefetching, and using fused transformation ops. Access additional resources and performance guides to further enhance your distributed TensorFlow training skills.