Training machine learning models at scale often requires significant investments. In this session, we show how Amazon SageMaker enables you to reduce time and costs to train and tune machine learning (ML) models without the need to manage infrastructure. Learn how to use models using built-in tools to manage and track training experiments, automatically choose optimal hyperparameters, debug training jobs, and monitor the utilization of system resources such as GPUs, CPUs, and network bandwidth. We show how SageMaker Training tools enables faster distributed training, including libraries for data parallelism and model parallelism, and the Amazon SageMaker distributed training libraries automatically split models and training datasets across GPU instances to help you complete distributed training faster. Download slides »
Speaker: Gaurav Singh, Solutions Architect, AWS India
Duration: 30mins
