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Resource utilization

As explained in Machine Learning Platform Engineering

Resource utilization is a measurement of the computational resources consumed by a running application, such as CPU, memory, or capacity.

Resource utilization measures the CPU, memory, and capacity consumed by a running application. In production API deployments, these resources have configured limits, so monitoring them helps preserve performance and allocate capacity effectively. section 11.1.1

Basic resource monitoring connects deployment constraints to operational decisions:

  • Track CPU and memory against the limits assigned during deployment. section 11.1.1
  • Consider pod-scaling limits when evaluating whether an application can absorb additional load. section 11.1.1
  • Scrape BentoML's built-in /metrics endpoint with Prometheus and visualize utilization in Grafana. section 11.1.1
  • Review utilization alongside response latency and failing request counts to identify performance and reliability problems. section 11.1.1
Figure 11.5 BentoML basic monitoring dashboard
A screenshot of a computer

AI-generated content may be incorrect.

Use the BentoML basic monitoring dashboard to inspect resource and request metrics together. section 11.1.1

How Machine Learning Platform Engineering covers it

How deep it goes
Defines basic CPU, memory, and capacity monitoring, then connects it to Prometheus and Grafana without detailing metric internals.
Examples
Uses BentoML deployments, Prometheus scraping, Grafana dashboards, and Kubernetes pod capacity as operational examples.
What it assumes
Assumes a deployed API endpoint with configured CPU, memory, and pod-scaling limits.
What you can do after
Operate/configure Prometheus and Grafana monitoring for deployed BentoML applications and inspect utilization alongside request health.
How it connects
Connects resource utilization with request metrics, autoscaling capacity, observability, alerting, and service reliability.

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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.