Merge pull request #106 from opendatahub-io-contrib/hotfix/uwm

hotfix in uwm url

docs/odh-rhoai/kserve-uwm-dashboard-metrics.md

@@ -8,6 +8,8 @@ Enabling RHOAI User Workload Metrics for Single Serving Models and deploying a G
 
 By monitoring these metrics, you can identify bottlenecks, optimize resource allocation, and ensure efficient infrastructure utilization. This enables data-driven decisions to improve the overall performance and scalability of your AI applications.
 
+* *[RHOAI Metrics Dashboard for Single Serving Models Code Repository](https://github.com/rh-aiservices-bu/rhoai-uwm)*
+
 ### Prerequisites
 
 - OpenShift 4.10 or later
@@ -21,7 +23,7 @@ To enable RHOAI User Workload Metrics for Single Serving Models and deploy the G
 
 * [Configure Monitoring for the Single Model Serving Platform](https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.11/html/serving_models/serving-large-models_serving-large-models#configuring-monitoring-for-the-single-model-serving-platform_serving-large-models)
 * [Configure GPU Monitoring Dashboard](https://docs.nvidia.com/datacenter/cloud-native/openshift/23.9.2/enable-gpu-monitoring-dashboard.html)
-* [Install the RHOAI Metrics Grafana and Dashboards for Single Serving Models](./rhoai-uwm-grafana/README.md)
+* [Install the RHOAI Metrics Grafana and Dashboards for Single Serving Models](https://github.com/rh-aiservices-bu/rhoai-uwm/blob/main/rhoai-uwm-grafana/README.md)
 
 ### Configure Monitoring for the Single Model Serving Platform
 
@@ -67,4 +69,4 @@ The following dashboards are currently available:
 
 ### Conclusion
 
-By turning on RHOAI User Workload Metrics and setting up the Grafana Dashboard, you can easily track how your Single Serving Models are doing and what resources they're using. It helps you find problems, tweak resource use, and make better choices to keep your AI apps running smoothly.
+By turning on RHOAI User Workload Metrics and setting up the Grafana Dashboard, you can easily track how your Single Serving Models are doing and what resources they're using. It helps you find problems, tweak resource use, and make better choices to keep your AI apps running smoothly.