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

Introduction to AIOps

• Defining AIOps and its strategic importance
• AIOps architecture and essential components

Gathering and Standardizing Operational Data

• Categories of observability data: metrics, logs, and traces
• Ingesting data from diverse sources (servers, containers, cloud)
• Utilizing agents and exporters (Prometheus, Beats, Fluentd)

Data Correlation and Anomaly Identification

• Time-series correlation and statistical techniques
• Applying ML models for anomaly detection
• Identifying incidents within distributed systems

Alerting and Noise Mitigation

• Crafting intelligent alert rules and thresholds
• Suppression, deduplication, and alert grouping strategies
• Integration with Alertmanager, Slack, PagerDuty, or Opsgenie

Root Cause Analysis and Visualization

• Leveraging dashboards to visualize metrics and spot trends
• Investigating events and timelines for RCA
• Tracking issues across layers using distributed tracing tools

Automation and Remediation

• Triggering automated scripts or workflows from incidents
• Integration with ITSM systems (ServiceNow, Jira)
• Use cases: self-healing, scaling, and traffic rerouting

Open Source and Commercial AIOps Platforms

• Tool overview: Prometheus, Grafana, ELK, Moogsoft, Dynatrace
• Criteria for evaluating and selecting an AIOps platform
• Demonstration and hands-on practice with a chosen stack

Wrap-up and Future Steps