Deploying quantized models on Amazon SageMaker AI with Unsloth
In this post, you will learn four deployment patterns for taking models that have already been quantized with Unsloth and deploying them on AWS infrastructure.
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In this post, you will learn four deployment patterns for taking models that have already been quantized with Unsloth and deploying them on AWS infrastructure.
The patterns use Amazon Elastic Compute Cloud (Amazon EC2) for direct instance access, Amazon SageMaker AI inference endpoints for managed serving, and Amazon Elastic Kubernetes Service (Amazon EKS) or...
You also learn operational practices for production deployments.
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The useful part
In this post, you will learn four deployment patterns for taking models that have already been quantized with Unsloth and deploying them on AWS infrastructure. The patterns use Amazon Elastic Compute Cloud (Amazon EC2) for direct instance access, Amazon SageMaker AI inference endpoints for managed serving, and Amazon Elastic Kubernetes Service (Amazon EKS) or Amazon Elastic Container Service (Amazon ECS) when inference needs to fit into an existing container framework. You also learn operational practices for production deployments.
How it works
- The patterns use Amazon Elastic Compute Cloud (Amazon EC2) for direct instance access, Amazon SageMaker AI inference endpoints for managed serving, and Amazon Elastic Kubernetes Service (Amazon EKS) or...
Details worth keeping
You also learn operational practices for production deployments.
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