An ML engineer uses one ML framework to train multiple ML models. The ML engineer needs to optimize the inference costs and host the models on Amazon SageMaker AI. Which solution will meet these…

The correct answer is B. Create a multi-model inference endpoint for all the models. A multi-model inference endpoint allows multiple models that use the same ML framework to share the same endpoint and underlying compute resources, significantly reducing hosting costs by loading models on demand instead of running separate infrastructure for each model.

Deployment and Orchestration of ML Workflows

Question

An ML engineer uses one ML framework to train multiple ML models. The ML engineer needs to optimize the inference costs and host the models on Amazon SageMaker AI. Which solution will meet these requirements MOST cost-effectively?

Options

ACreate a multi-container inference endpoint for direct invocation.
BCreate a multi-model inference endpoint for all the models.
CCreate a multi-container inference endpoint for sequential invocation.
DCreate multiple single-model inference endpoint for each model.

How the community answered

(61 responses)

A
5% (3)
B
72% (44)
C
8% (5)
D
15% (9)

Explanation

A multi-model inference endpoint allows multiple models that use the same ML framework to share the same endpoint and underlying compute resources, significantly reducing hosting costs by loading models on demand instead of running separate infrastructure for each model.

Topics

#SageMaker Inference#Multi-Model Endpoint#Cost Optimization#Deployment Strategies

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