Deploy and evaluate models with Amazon SageMaker JumpStart

This article demonstrates how to deploy pretrained models from Amazon SageMaker JumpStart, run performance tests, and clean up endpoints using the SageMaker Python SDK.

• Discover models from SageMaker JumpStart catalog using UI or Python SDK filters
• Deploy models with recommended instance types and JumpStart configurations
• Run sample prompts and measure latency, output length, and generation rate
• Extract and parse model responses across different response formats
• Clean up endpoints after testing to avoid unnecessary costs
• 2026 improvements include Model Hub browsing, pre-configured deployment options, and Hugging Face deep linking

This reusable workflow enables practical model evaluation and selection based on performance metrics across SageMaker JumpStart's catalog of generative AI models.