In November 2024, Jayissi developed a model validation enhancement for the microsoft/onnxruntime-genai repository, focusing on integrating perplexity metrics into the existing validation workflow. Using Python and leveraging skills in data analysis and machine learning, Jayissi implemented automatic perplexity calculation to support comprehensive performance reporting and ongoing model monitoring. The solution aligned validation metrics with governance requirements, enabling more robust model risk assessment and quality control. By validating the integration within the current pipeline, Jayissi ensured that perplexity-based insights could be used for release validation and monitoring, demonstrating depth in model validation and workflow integration over the course of the month.