AIOps Fundamentals: Monitoring, Correlation, and Intelligent Alerting Training Course

Introduction to AIOps

• Defining AIOps and understanding its significance
• Contrasting traditional monitoring with AIOps-driven observability
• Overview of AIOps architecture and essential components

Collecting and Normalizing Operational Data

• Observability data types: metrics, logs, and traces
• Ingesting data from diverse sources such as servers, containers, and cloud environments
• Utilizing agents and exporters (e.g., Prometheus, Beats, Fluentd)

Data Correlation and Anomaly Detection

• Time series correlation and statistical analysis methods
• Applying ML models for effective anomaly detection
• Identifying incidents across distributed systems

Alerting and Noise Reduction

• Crafting intelligent alert rules and thresholds
• Implementing suppression, deduplication, and alert grouping
• Integration with platforms like Alertmanager, Slack, PagerDuty, or Opsgenie

Root Cause Analysis and Visualization

• Leveraging dashboards to visualize metrics and identify trends
• Analyzing events and timelines for Root Cause Analysis (RCA)
• Tracking issues across layers using distributed tracing tools

Automation and Remediation

• Triggering automated scripts or workflows from detected incidents
• Integration with ITSM systems such as ServiceNow and Jira
• Real-world use cases: self-healing, auto-scaling, and traffic rerouting

Open Source and Commercial AIOps Platforms

• Overview of key tools: Prometheus, Grafana, ELK, Moogsoft, and Dynatrace
• Criteria for evaluating and selecting an AIOps platform
• Demonstration and hands-on practice with a selected stack

Summary and Next Steps