Over a two-month period, this developer focused on enhancing deployment stability and automation for AWS-based machine learning workflows. In the aws-samples/sagemaker-genai-hosting-examples repository, they stabilized SageMaker deployments by pinning SGLang to a specific Docker image version, reducing environment drift and improving reproducibility. The following month, they contributed to awslabs/amazon-bedrock-agentcore-samples by implementing CloudFormation-based automated deployment for the runtime-gateway example, streamlining cloud onboarding and reliability. Their work included Python scripting, Dockerfile updates, and code hygiene improvements such as typo corrections, file renaming to resolve circular imports, and navigation path updates, resulting in more maintainable and production-ready deployment pipelines.