📄 中文摘要
通过搜索可用的 p-family GPU 容量，创建推理的训练计划预留，并在该预留容量上部署 SageMaker AI 推理端点，展示了数据科学家在模型评估过程中如何预留计算资源并管理端点的整个生命周期。该过程强调了有效利用 GPU 资源的重要性，并提供了实际操作的指导，确保推理服务的高效性和稳定性。
使用训练计划设置 GPU 容量部署 SageMaker AI 推理端点
📄 English Summary
Deploy SageMaker AI inference endpoints with set GPU capacity using training plans
The process involves searching for available p-family GPU capacity, creating a training plan reservation for inference, and deploying a SageMaker AI inference endpoint on that reserved capacity. It illustrates a data scientist's journey in reserving resources for model evaluation and managing the endpoint throughout its lifecycle. The emphasis is on the importance of efficiently utilizing GPU resources and provides practical guidance for ensuring the effectiveness and stability of inference services.