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

Introduction to AIOps

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

Gathering and Standardizing Operational Data

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

Data Correlation and Anomaly Identification

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

Alerting and Noise Mitigation

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

Root Cause Analysis and Visualization

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

Automation and Remediation

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

Open Source and Commercial AIOps Platforms

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

Conclusion and Future Directions