AIOps Foundations - Intelligent Monitoring With Prometheus & Grafana

This comprehensive course provides a practical introduction to AIOps (Artificial Intelligence for IT Operations) for DevOps engineers, SREs, and IT professionals. Participants will learn how to build intelligent monitoring systems that go beyond static threshold alerts. Through hands-on labs, learners will deploy Prometheus and Grafana stacks, collect system metrics, master PromQL queries, and implement AI-powered anomaly detection and forecasting using Python and open-source ML libraries. The course follows the AIOps Pyramid framework: High-Quality Data, AI-Driven Insights, and Intelligent Actions. Ideal for professionals looking to transform reactive monitoring into proactive, AI-enhanced operations.

Course Highlights:

1. The "AI" in AIOps: From Data to Decisions

• Introduction to AIOps and its value proposition for IT Operations
• The AIOps Pyramid: Data Foundation, AI-Driven Insights, and Intelligent Actions
• Understanding why metrics are ideal for machine learning
• Overview of Prometheus and Grafana monitoring stack
• Deploying a production-grade monitoring environment

2. Collecting the Data Fuel: Prometheus & Exporters

• Understanding Prometheus's pull-based metrics collection model
• The Prometheus exposition format and metric types
• Configuring scrape jobs and static targets
• Deploying Node Exporter for system-level metrics
• The exporter pattern and its advantages for universal monitoring

3. Basic Analysis with PromQL & The Limits of Manual Thresholds

• Introduction to PromQL for time-series analysis
• Writing queries with label filtering and aggregations
• Converting counter metrics into meaningful rates
• Calculating resource usage from raw metrics
• Understanding the limitations of static threshold alerts

4. AI-Powered Anomaly Detection

• The problems with threshold-based monitoring in dynamic environments
• Setting up Python ML environment with scikit-learn
• Training IsolationForest models for unsupervised anomaly detection
• Feature engineering for time-series data
• Real-time anomaly detection on monitoring metrics

5. AI-Driven Forecasting for Proactive Operations

• From reactive to predictive operations with forecasting
• Setting up Python forecasting environment with Prophet
• Training additive time-series models
• Generating forecasts with confidence intervals
• Capacity planning and predicting resource exhaustion