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PodMonitor

As explained in Machine Learning Platform Engineering

A PodMonitor is a Prometheus Operator custom resource that specifies how to discover pods and scrape their exposed metrics.

A PodMonitor is a Kubernetes custom resource that tells Prometheus how to discover pods and scrape their metrics. It selects pods, ports, endpoints, and scrape frequency, simplifying monitoring for dynamically changing deployments. section 11.1.1

A practical verification workflow checks the entire monitoring path:

  • Open Status, then Service Discovery, and confirm a PodMonitor monitors the deployment. section 11.1.1
  • Search for application metrics in Prometheus before visualizing them in Grafana. section 11.1.1

Monitoring BentoML deployments

A PodMonitor can monitor all BentoML deployments, allowing Prometheus to collect metrics exposed through their /metrics endpoints and making those metrics available for Grafana dashboards. section 11.1.1

How Machine Learning Platform Engineering covers it

How deep it goes
Explains resource fields and verification workflow, but does not detail the manifest contents or Prometheus internals.
Examples
Uses BentoML deployments, a /metrics endpoint, Prometheus searches, and Grafana visualization as concrete artifacts.
What it assumes
Assumes a deployed application exposes /metrics and access to Kubernetes, Prometheus, and Grafana services.
What you can do after
Operate/configure PodMonitor scraping and verify collected application metrics through Prometheus and Grafana.
How it connects
Connects Kubernetes pods, Prometheus, metrics endpoints, Service Discovery, Grafana, and PromQL dashboards.
Cover of Machine Learning Platform Engineering
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Machine Learning Platform Engineering. The section links point to the parts of the book it draws on.