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
• Examining AIOps architecture and core components

Operational Data Collection and Standardization

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

Data Correlation and Anomaly Identification

• Time series correlation and statistical analysis methods
• Applying ML models for anomaly detection
• Identifying incidents within distributed system architectures

Intelligent Alerting and Noise Mitigation

• Crafting intelligent alert rules and threshold definitions
• Techniques for suppression, deduplication, and alert grouping
• Integration with Alertmanager, Slack, PagerDuty, or Opsgenie

Root Cause Analysis and Data Visualization

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

Automation and Remediation Strategies

• Initiating automated scripts or workflows triggered by incidents
• Integration with ITSM systems (ServiceNow, Jira)
• Practical applications: self-healing, auto-scaling, and traffic rerouting

Open Source and Commercial AIOps Ecosystems

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

Summary and Future Directions