Deploy and Evaluate Models with Amazon SageMaker JumpStart

What happened

Amazon SageMaker JumpStart provides a workflow to deploy a pretrained model, run a repeatable performance test with the SageMaker Python SDK, and delete the endpoint afterward. The test measures latency, estimated output length, and single-request generation rate using sample prompts. Model Hub discovery and preconfigured endpoint options simplify browsing and deployment, while a deep link integration lets users deploy from Hugging Face model discovery in a single click.

The story so far

AWS published a SageMaker JumpStart workflow that shows users how to deploy a pretrained model, test its performance with the SageMaker Python SDK, and delete the endpoint when testing is complete. The test uses sample prompts to measure latency, estimated output length, and the generation rate for a single request. Users can find models through Model Hub and use preconfigured endpoint options; a Hugging Face integration also supports deployment from model discovery in a single click. AWS says the workflow applies to supported models when capacity is available, with deployment time depending on the model, instance type, quota, and AWS Region. Reusing the test across compatible JumpStart models may require adjustments to payload formats, instance types, end-user license agreement requirements, and response schemas.

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