Deploy and evaluate models with Amazon SageMaker JumpStart
In this post, we show how to deploy an available model from SageMaker JumpStart, run a lightweight and repeatable performance test using the SageMaker Python SDK, and delete the endpoint when testing is complete.
Proof1 independent outlet
Key points
- Choosing the right model for a specific application requires a practical way to test it with representative inputs.
- Amazon SageMaker JumpStart provides access to a broad catalog of already pretrained models.
- In 2026, SageMaker JumpStart introduced two improvements that simplify model discovery and deployment.
- Discover and deploy a model from SageMaker JumpStart.
Sources (1)
- [1]Deploy and evaluate models with Amazon SageMaker JumpStartAWS Machine Learning Blog · Oct 14, 01:14 PM
In this post, we show how to deploy an available model from SageMaker JumpStart, run a lightweight and repeatable performance test using the SageMaker Python SDK, and delete the endpoint when testing is complete.
Choosing the right model for a specific application requires a practical way to test it with representative inputs.
Extractive summary: sentences quoted from the sources.
Before this
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