Summary
This chapter focused on the operational tasks related to running and serving ML models on OpenShift and OpenShift Data Science. You have learned that Red Hat OpenShift Data Science comes with a Prometheus instance. You have also learned how to set up Grafana to visualize the Prometheus data.
We have talked about the importance of logging and how it is different from monitoring and traditional software application logging. You have also learned how to enable the ModelMesh payload processors to achieve payload logging.
We have also learned that the current version of ODS does not yet contain a feature for configuring the logging dimension of model servers through the web console.
As part of your learning, we encourage you to experiment with the configurations beyond what was described in the book. There is a lot more to learn about Grafana and Prometheus. You can explore other metrics in Prometheus and create custom dashboards in Grafana. We also encourage you to experiment...
