AI Infrastructure Monitoring
Build comprehensive monitoring for AI and ML infrastructure using industry-standard tools. Learn to deploy Prometheus for metrics collection, create Grafana dashboards for GPU clusters, configure intelligent alerting for training jobs and inference endpoints, and establish monitoring best practices that scale with your ML platform.
What You'll Learn
This course covers end-to-end monitoring for AI infrastructure from metrics collection to actionable dashboards.
Prometheus Metrics
Deploy and configure Prometheus to collect GPU, CPU, memory, and custom ML metrics from training and inference workloads.
Grafana Dashboards
Create rich visualizations for ML infrastructure health, training progress, model serving latency, and resource utilization.
Intelligent Alerting
Configure alerts that matter: GPU failures, training stalls, inference latency spikes, and resource exhaustion warnings.
Production Patterns
Learn monitoring patterns for multi-cluster AI platforms, high-availability setups, and cost-effective long-term storage.
Course Lessons
Follow the lessons in order for a comprehensive understanding of AI infrastructure monitoring.
1. Introduction
Why monitoring matters for AI infrastructure, key metrics to track, and the observability stack for ML platforms.
2. Prometheus
Deploy Prometheus for ML workloads, configure service discovery, scrape GPU metrics, and set up recording rules.
3. Grafana
Build Grafana dashboards for AI infrastructure: GPU utilization, training progress, model serving performance, and cluster health.
4. Alerts
Design alerting rules for ML infrastructure: GPU failures, OOM kills, training job failures, and SLO-based alerts.
5. Dashboards
Advanced dashboard design: multi-cluster views, training experiment tracking, capacity planning, and executive summaries.
6. Best Practices
Production monitoring patterns: high availability, long-term storage, multi-tenancy, and monitoring-as-code.
Prerequisites
What you need before starting this course.
- Basic understanding of Kubernetes and containerized applications
- Familiarity with metrics concepts (counters, gauges, histograms)
- Access to a Kubernetes cluster for hands-on exercises
- Basic knowledge of ML training and serving workflows