Deploy SageMaker AI inference endpoints with set GPU capacity using training plans

This article provides a step-by-step guide on deploying SageMaker AI inference endpoints with reserved GPU capacity using training plans, allowing data scientists to manage and optimize their model evaluation and deployment process. By reserving GPU capacity, data scientists can ensure efficient and cost-effective model inference, which is crucial for large-scale AI applications.

⚡ Key Takeaways

• Data scientists can search for available p-family GPU capacity to reserve for inference endpoints
• Training plan reservations can be created for inference to manage and optimize model evaluation
• SageMaker AI inference endpoints can be deployed on reserved GPU capacity for efficient model inference

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