Deploy and evaluate models with Amazon SageMaker JumpStart
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This article outlines a workflow for deploying and evaluating machine learning models using Amazon SageMaker JumpStart. It highlights recent 2026 improvements, including enhanced model discovery in the Model Hub and one-click deployment from Hugging Face. The proposed solution utilizes a reusable Python script to discover, deploy, test, and clean up models via the SageMaker Python SDK. Users can browse the catalog or filter models programmatically before deploying them with pre-configured endpoint options optimized for specific priorities like latency or cost. The evaluation phase involves running sample prompts to measure key performance metrics such as latency, output length, and generation rate. Finally, the process concludes with deleting the endpoint to ensure resource cleanup. This repeatable pattern allows developers to compare different models based on quality, speed, and cost efficiency.
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