Learn how Amazon SageMaker AI transforms model development with a unified environment for all your AI workloads - from interactive model development on IDEs to maximizing task and compute resource utilization across training and inference workloads. We'll demonstrate how to use SageMaker Studio as the familiar IDE to develop, submit, and monitor ML jobs, while leveraging the scalability and resiliency of HyperPod environment for computationally intensive tasks like training and deploying large foundation models. You will also learn how HyperPod task governance capability can help you automatically allocate compute resources based on task prioritization end-to-end through the model development lifecycle.

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