Deploy and evaluate models with Amazon SageMaker JumpStart
Learn how to deploy an available model from Amazon SageMaker JumpStart, run a lightweight and repeatable performance test using the SageMaker Python SDK, and delete the endpoint when testing is complete.
Amazon SageMaker JumpStart offers a catalog of pretrained models that can be deployed and tested for performance evaluation. This process involves discovering and deploying a model, running sample prompts, measuring latency and output characteristics, and then cleaning up the endpoint. The workflow is repeatable, although deployment time depends on factors like model, instance type, quota, and AWS Region.
In 2026, SageMaker JumpStart introduced improvements that simplify model discovery, deployment, and a deep link integration between Hugging Face and SageMaker AI deployment. To execute this workflow, prerequisites include an AWS account with SageMaker AI permissions, Python 3+ with the SageMaker Python SDK installed, and sufficient GPU instance quota.
Steps include discovering available models via the UI or Python SDK, deploying the model using recommended instance types, running sample prompts to measure performance, and finally deleting the endpoint once testing is complete.
Written by urgent.news from AWS Machine Learning's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
