AWS sagemaker documentation change
Summary
Replaced 'leverage' with 'use' in the model deployment description table.
Security assessment
Minor wording change with no security impact. No security features or configurations are mentioned.
Diff
diff --git a/sagemaker/latest/dg/sagemaker-hyperpod-model-deployment-deploy.md b/sagemaker/latest/dg/sagemaker-hyperpod-model-deployment-deploy.md index 6cc857128..be277dfd3 100644 --- a//sagemaker/latest/dg/sagemaker-hyperpod-model-deployment-deploy.md +++ b//sagemaker/latest/dg/sagemaker-hyperpod-model-deployment-deploy.md @@ -13 +13 @@ Whether you're deploying pre-trained foundation open-weights or gated models fro -**Description** | Deploy from a comprehensive catalog of pre-trained foundation models with automatic optimization and scaling policies tailored to each model family. | Bring your own custom and fine-tuned models and leverage SageMaker HyperPod's enterprise infrastructure for production-scale inference. Choose between cost-effective storage with Amazon S3 or a high-performance file system with Amazon FSx. | Load model weights from a node's local NVMe storage to eliminate network latency during pod startup. Useful for autoscaling events, scale-from-zero workloads, and latency-sensitive failovers. +**Description** | Deploy from a comprehensive catalog of pre-trained foundation models with automatic optimization and scaling policies tailored to each model family. | Bring your own custom and fine-tuned models and use SageMaker HyperPod's enterprise infrastructure for production-scale inference. Choose between cost-effective storage with Amazon S3 or a high-performance file system with Amazon FSx. | Load model weights from a node's local NVMe storage to eliminate network latency during pod startup. Useful for autoscaling events, scale-from-zero workloads, and latency-sensitive failovers.