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

Introduction to AIOps

• Defining AIOps and its strategic importance
• Comparing traditional monitoring with AIOps-driven observability
• Architectural components and core AIOps structures

Gathering and Standardizing Operational Data

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

Data Correlation and Anomaly Identification

• Time series correlation and statistical analysis methods
• Applying ML models for detecting anomalies
• Identifying incidents within distributed systems

Alerting Strategies and Noise Mitigation

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

Root Cause Analysis and Visual Representation

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

Automation and Remediation Workflows

• Executing automated scripts or workflows triggered by incidents
• Connecting with ITSM systems (ServiceNow, Jira)
• Application scenarios: self-healing, scaling, and traffic rerouting

Open Source and Commercial AIOps Solutions

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

Conclusion and Future Directions