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

Introduction to AIOps

• Defining AIOps and its importance
• Contrasting traditional monitoring with AIOps-driven observability
• Understanding AIOps architecture and key components

Collecting and Normalising Operational Data

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

Data Correlation and Anomaly Detection

• Time series correlation and statistical methodologies
• Applying ML models for anomaly detection
• Identifying incidents within distributed systems

Alerting and Noise Reduction

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

Root Cause Analysis and Visualisation

• Using dashboards to visualise metrics and detect trends
• Analysing events and timelines for RCA
• Tracing issues across layers with distributed tracing tools

Automation and Remediation

• Triggering 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
• Evaluation criteria for selecting an AIOps platform
• Demonstration and hands-on practice with a selected stack

Summary and Next Steps