Beam model by Reflection is now available on Amazon SageMaker JumpStart and AWS Trainium
Sagemaker Deploy ยท 2026-10-21
Actions
Technical Details
| Regions | us-east-1, us-east-2 |
|---|---|
| Cost Impact | Neutral |
What This Means
For DevOps Teams
Deploy the Beam model on Amazon SageMaker JumpStart and AWS Trainium to enable fine-tuning and customization of AI models, ensuring they align with your organization's specific requirements and security standards.
For Platform Teams
Adopt the Beam model on Amazon SageMaker JumpStart to provide your teams with powerful, open-weight AI models that can be integrated into existing workflows and scaled to meet production demands.
For Executives
Evaluate the integration of the Beam model into Amazon SageMaker JumpStart to leverage advanced, customizable AI capabilities that can enhance operational efficiency and innovation within your organization.
Source
Related Sagemaker Deploy Updates
- Amazon SageMaker AI announces training session APIs for serverless model customization (2026-10-22)
- Announcing new ways to customize models with SageMaker AI, without managing GPUs (2026-10-22)
- Amazon SageMaker AI now supports serverless model customization for Muse Glimmer, Qwen 3.8 and Ministral 3 models (2026-10-22)
- CVE-2026-112408 - Missing bucket ownership verification in Amazon SageMaker Python SDK default_bucket() (2026-10-21)
- Amazon SageMaker AI now supports custom sequence lengths for serverless model customization jobs (2026-10-21)