Kerr Ding enhanced observability for inference workloads in the intel/ai-reference-models repository by delivering a targeted improvement to model inference performance logging. He refined CPU core detection logic within multiple run_model.sh scripts, leveraging his expertise in bash scripting and Linux command line tools to increase the accuracy and reliability of performance metrics. This work standardized logging instrumentation across inference workflows, enabling more consistent and actionable data collection. By validating end-to-end metric gathering with representative workloads, Kerr’s contribution supported data-driven performance optimization and facilitated faster bottleneck identification, ultimately enabling better resource planning and laying groundwork for potential cost-efficiency improvements.