During July 2025, Davide Gallitelli developed and documented an end-to-end SageMaker deployment tutorial for the HuggingFaceTB/SmolLM3-3B model in the aws-samples/sagemaker-genai-hosting-examples repository, using Python, Docker, and Shell scripting. He enhanced the accompanying Jupyter notebook by embedding image attachments to improve visualization and onboarding for new users. In the strands-agents/sdk-python repository, Davide integrated Amazon SageMaker AI endpoints as a model provider, implementing a new SageMakerAIModel class with supporting dependencies and comprehensive unit and integration tests. His work accelerated model deployment workflows, expanded deployment options, and improved testing coverage, demonstrating depth in machine learning deployment and SDK development.