Category: Amazon SageMaker HyperPod

Accelerate AI model development with new training features that enable instant recovery from failures and automatic scaling based on resource availability.

New program gives organizations unprecedented access to Nova model training, enabling them to build custom frontier models that deeply embed domain expertise without the traditional barriers of cost, compute, and time.

Let me start this week’s update with something I’m especially excited about – the upcoming BeSA (Become a Solutions Architect) cohort. BeSA is a free mentoring program that I host along with a few other AWS employees on a volunteer basis to help people excel in their cloud careers. Last week, the instructors’ lineup was […]

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.

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.