In today's High-Performance Computing (HPC) systems, application performance variations are among the most vital challenges as they adversely affect system efficiency, application performance, and cost. System administrators need to identify the anomalies that are responsible for performance variation and take mitigating actions. One can perform manual root-cause analysis on telemetry data collected by HPC monitoring infrastructures to analyze performance variations. However, manual analysis methods are time-intensive and limited in impact due to the increasing complexity of HPC systems and terabyte/day-sized telemetry data. State-of-the-art approaches use machine learning-based methods to diagnose performance anomalies automatically. This paper deploys an end-to-end machine learning framework that diagnoses performance anomalies on compute nodes on a 1488-node production HPC system. We demonstrate job and node-level anomaly diagnosis results with the Grafana frontend interface at runtime. Furthermore, we discuss challenges and design decisions for the deployment.