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 its essential components

Collecting and Normalising Operational Data

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

Data Correlation and Anomaly Detection

• Applying time series correlation and statistical methods
• Deploying 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
• Integration with Alertmanager, Slack, PagerDuty, or Opsgenie

Root Cause Analysis and Visualisation

• Using dashboards to visualise metrics and uncover trends
• Analysing events and timelines for RCA (Root Cause Analysis)
• Tracing issues across system layers using distributed tracing tools

Automation and Remediation

• Triggering automated scripts or workflows in response to incidents
• Integrating with ITSM systems (e.g., ServiceNow, Jira)
• Practical use cases: self-healing, auto-scaling, and traffic rerouting

Open Source and Commercial AIOps Platforms

• Overview of key tools: Prometheus, Grafana, ELK, Moogsoft, Dynatrace
• Establishing evaluation criteria for selecting an AIOps platform
• Demonstrations and hands-on exercises with a chosen stack

Summary and Recommended Next Steps