Are you looking to customize foundation models and deploy AI agents at scale? Learn how to leverage Amazon SageMaker AI to build performant agentic workflows with customized open-weight models. This session covers the end-to-end journey: use Amazon SageMaker AI for model customization, track experiments with managed MLflow, establish governance with Amazon SageMaker AI Model Registry, and deploy optimized models using SGLang on Amazon SageMaker AI Inference for low-latency agent applications. Discover how to create repeatable, auditable workflows with Amazon SageMaker AI Pipelines while maintaining comprehensive observability and control for production AI systems. Includes a live demonstration of customizing and deploying an open-weight model, plus insights from SGLang's team on inference for agents.

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