Category: Amazon SageMaker AI

AWS now enables extensive customization of Amazon Nova foundation models through SageMaker AI with techniques including continued pre-training, supervised fine-tuning, direct preference optimization, reinforcement learning from human feedback and model distillation to better address domain-specific requirements across industries.

DeepSeek-R1, a powerful large language model featuring reinforcement learning and chain-of-thought capabilities, is now available for deployment via Amazon Bedrock and Amazon SageMaker AI, enabling users to build and scale their generative AI applications with minimal infrastructure investment to meet diverse business needs.

Amazon SageMaker HyperPod recipes help customers get started with training and fine-tuning popular publicly available foundation models, like Llama 3.1 405B, in just minutes with state-of-the-art performance.

Unlock efficient large model training with SageMaker HyperPod flexible training plans – find optimal compute resources and complete training within timelines and budgets.

Enable priority-based resource allocation, fair-share utilization, and automated task preemption for optimal compute utilization across teams.

Amazon SageMaker HyperPod’s integration with Amazon EKS brings resilience, observability, and flexibility to large model training, reducing downtime by up to 40%.

Streamline your ML workflows with this generative AI assistant providing tailored guidance, code generation, and error troubleshooting, to build, train, and deploy models efficiently.

Today, we are introducing Amazon SageMaker HyperPod, which helps reducing time to train foundation models (FMs) by providing a purpose-built infrastructure for distributed training at scale. You can now use SageMaker HyperPod to train FMs for weeks or even months while SageMaker actively monitors the cluster health and provides automated node and job resiliency by […]