Today, we’re announcing the general availability of Amazon SageMaker HyperPod flexible training plans to help data scientists train large foundation models (FMs) within their timelines and budgets and save them weeks of effort in managing the training process based on compute availability. At AWS re:Invent 2023, we introduced SageMaker HyperPod to reduce the time to train FMs by up to 40 percent and scale across thousands of compute resources in parallel with preconfigured distributed training libraries and built-in resiliency. Most generative AI model development tasks need accelerated compute resources in parallel. Our customers struggle to find timely access to compute resources to complete their training within their timeline and budget constraints. With today’s announcement, you can find the required accelerated…

Today, we’re announcing the general availability of Amazon SageMaker HyperPod task governance, a new innovation to easily and centrally manage and maximize GPU and Tranium utilization across generative AI model development tasks, such as training, fine-tuning, and inference. Customers tell us that they’re rapidly increasing investment in generative AI projects, but they face challenges in efficiently allocating limited compute resources. The lack of dynamic, centralized governance for resource allocation leads to inefficiencies, with some projects underutilizing resources while others stall. This situation burdens administrators with constant replanning, causes delays for data scientists and developers, and results in untimely delivery of AI innovations and cost overruns due to inefficient use of resources. With SageMaker HyperPod task governance, you can accelerate time…