Training large AI models requires significant compute resources and can be time and cost intensive. In this session, learn to optimize and accelerate your model training workloads using AWS's purpose-built infrastructure and tools. We'll dive deep into leveraging services like Amazon SageMaker HyperPod for distributed training at scale and SageMaker fully managed training jobs for cost-effective ML acceleration. You'll learn to scale training across clusters using techniques like data and model parallelism, automated model tuning, and efficient checkpoint management. Through real customer examples, see how to reduce training time by up to 40%, optimize costs, and build high-performance training pipelines.

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