Worked on the Azure/azureml-examples repository to deliver two feature enhancements focused on deployment security and training workflow reliability. Introduced a parameter for Key Vault role-based access control and upgraded the Docker image version, improving both security and compatibility for cloud deployments. Updated the training pipeline configuration to install the protobuf dependency, ensuring robust execution of machine learning training scripts. The work leveraged Azure, DevOps, and Python scripting, with YAML used for pipeline configuration. These targeted updates addressed reproducibility and security requirements, reflecting a methodical approach to cloud-based machine learning workflows and infrastructure management within the Azure ecosystem.