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

Introduction to AIOps

• Defining AIOps and its strategic importance
• Contrasting traditional monitoring with AIOps-driven observability
• AIOps architecture and essential components

Collecting and Normalizing Operational Data

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

Data Correlation and Anomaly Detection

• Time series correlation and statistical approaches
• Employing ML models for anomaly detection
• Identifying incidents across distributed systems

Alerting and Noise Reduction

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

Root Cause Analysis and Visualization

• Visualizing metrics and identifying trends using dashboards
• Investigating events and timelines for RCA
• Tracking issues across layers with distributed tracing tools

Automation and Remediation

• Initiating automated scripts or workflows from incidents
• Connecting with ITSM systems (ServiceNow, Jira)
• Key use cases: self-healing, scaling, and traffic rerouting

Open Source and Commercial AIOps Platforms

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

Summary and Next Steps