Operating ML Workloads
In the previous chapter, you learned how to automate model deployments through OpenShift Data Science (ODS) pipelines. This chapter will focus on the operational tasks of MLOps. This includes monitoring and logging, using the in-built tools of Red Hat OpenShift Data Science. We will not cover the common OpenShift operation and administration tasks in this chapter as that is beyond the scope of this book. However, we will talk about some of the OpenShift concepts you need to know to understand the topics in this chapter.
The exercises in this chapter require a basic understanding of OpenShift and/or Kubernetes as well as basic knowledge of Prometheus time-series databases and Grafana visualization dashboards. The following topics will be covered in this chapter:
- Monitoring ML models
- Logging model inference
- Cost optimization
The materials required for this chapter can be found in the GitHub repository of this book. The files that you will...
