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

Introduction to AIOps

• Defining AIOps and its significance
• Traditional monitoring vs. AIOps-driven observability
• AIOps architecture and core components

Collecting and Normalizing Operational Data

• Types of observability data: metrics, logs, and traces
• Ingesting data from various sources (servers, containers, cloud)
• Employing agents and exporters (Prometheus, Beats, Fluentd)

Data Correlation and Anomaly Detection

• Time series correlation and statistical approaches
• Applying ML models for anomaly detection
• Identifying incidents within 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

• Utilizing dashboards to visualize metrics and identify trends
• Investigating events and timelines for RCA
• Tracking issues across layers with distributed tracing tools

Automation and Remediation

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

Open Source and Commercial AIOps Platforms

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

Summary and Next Steps