Amazon SageMaker HyperPod removes the undifferentiated heavy lifting involved in building generative AI models. It helps quickly scale model development tasks such as training, fine-tuning, or inference across a cluster of hundreds or thousands of AI accelerators. SageMaker HyperPod enables centralized governance across all your model development tasks, giving you full visibility and control over how different tasks are prioritized and how compute resources are allocated to each task, helping you maximize GPU and AWS Trainium utilization of your cluster and accelerate innovation.
Learn more about Amazon SageMaker HyperPod – http://go.aws/42Zlbsf
Subscribe to AWS: https://go.aws/subscribe
Sign up for AWS: https://go.aws/signup
AWS free tier: https://go.aws/free
Explore more: https://go.aws/more
Contact AWS: https://go.aws/contact
Next steps:
Explore on AWS in Analyst Research: https://go.aws/reports
Discover, deploy, and manage software that runs on AWS: https://go.aws/marketplace
Join the AWS Partner Network: https://go.aws/partners
Learn more on how Amazon builds and operates software: https://go.aws/library
Do you have technical AWS questions?
Ask the community of experts on AWS re:Post: https://go.aws/3lPaoPb
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—use AWS to be more agile, lower costs, and innovate faster.
#AWS #AmazonWebServices #modeltraining #AI #generativeAI #CloudComputing
