Davide Gallitelli developed and documented an end-to-end SageMaker deployment workflow for the aws-samples/sagemaker-genai-hosting-examples repository, focusing on deploying the SmolLM3-3B model with DJL-inference. He created a comprehensive tutorial, including a Dockerfile and Jupyter notebook, to streamline model deployment and testing on AWS SageMaker. Davide also enhanced notebook visualization by embedding image attachments, improving clarity for onboarding and demonstrations. In the strands-agents/sdk-python repository, he integrated Amazon SageMaker AI Endpoints as a model provider, adding a new class, dependencies, and thorough unit and integration tests. His work leveraged Python, Docker, and Boto3, deepening deployment and testing coverage.