Monitoring ML Systems in Production – Metrics, Logging, Alerting
30 Days of MLOps Challenge: Day 19
By Sandip Das · · 7 min read
💡
Hey — It's Sandip Das 👋
We must master monitoring to ensure ML models in production are healthy, reliable, and performant.
By tracking key metrics, analyzing logs, and setting up alerts, they can proactively detect model drift, data quality issues, and service failures before they impact users.
We must master monitoring to ensure ML models in production are healthy, reliable, and performant.
By tracking key metrics, analyzing logs, and setting up alerts, they can proactively detect model drift, data quality issues, and service failures before they impact users.
📚 Key Learnings
- Understand the importance of observability in ML systems: uptime, performance, correctness
- Learn what to monitor: prediction latency, model accuracy, data quality, service health
- Differentiate between system metrics and model metrics
- logging, dashboards, and alerting for deployed ML services
- Use Prometheus, Grafana, and custom logging solutions for insight and traceability
Read the full article on learnxops.com.
Topics: MLOps, Monitoring, Community, 30 Days of MLOps