Prometheus and Grafana for ML System Monitoring
Instrument Your FastAPI Model Server, Scrape It With Prometheus, and Build a Grafana Dashboard for Request Rate and Latency in One Docker Compose Stack.
By Sandip Das · · 4 min read
💡
Hey — It's Sandip Das 👋
In Day 42 we made data validation a hard gate in the DVC pipeline. That protects training, but once the model is serving, we still have no idea how fast it answers or how often it fails.
Today we cover the system-health pillar: instrument the FastAPI app, scrape it with Prometheus, learn the three PromQL functions you'll use daily, and build a Grafana dashboard.
In Day 42 we made data validation a hard gate in the DVC pipeline. That protects training, but once the model is serving, we still have no idea how fast it answers or how often it fails.
Today we cover the system-health pillar: instrument the FastAPI app, scrape it with Prometheus, learn the three PromQL functions you'll use daily, and build a Grafana dashboard.
📚 Key Learnings
- How Prometheus works: pull-based scraping, time series, labels
- The four metric types and which one latency needs
- PromQL basics:
rate(),increase()andhistogram_quantile() - Running FastAPI, Prometheus and Grafana together with Docker Compose
- Hands-on: a Grafana dashboard for request rate, error rate and p95 latency
🧠 Learn here
Why Prometheus f
Read the full article on learnxops.com.
Topics: 100 Days of MLOps, MLOps, ML, Monitoring