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

Introduction to AIOps

• Defining AIOps and understanding its significance
• Comparing traditional monitoring with AIOps-driven observability
• Examining AIOps architecture and core components

Collecting and Normalizing Operational Data

• Understanding types of observability data: metrics, logs, and traces
• Ingesting data from diverse sources such as servers, containers, and the cloud
• Utilizing agents and exporters (Prometheus, Beats, Fluentd)

Data Correlation and Anomaly Detection

• Applying time series correlation and statistical methods
• Leveraging ML models for effective anomaly detection
• Identifying incidents across distributed systems

Alerting and Noise Reduction

• Developing intelligent alert rules and thresholds
• Implementing suppression, deduplication, and alert grouping techniques
• Integrating with platforms like Alertmanager, Slack, PagerDuty, or Opsgenie

Root Cause Analysis and Visualization

• Visualizing metrics and detecting trends using dashboards
• Analyzing events and timelines to facilitate RCA
• Tracing issues across various layers using distributed tracing tools

Automation and Remediation

• Initiating automated scripts or workflows in response to incidents
• Integrating with ITSM systems such as ServiceNow or Jira
• Exploring use cases including self-healing, scaling, and traffic rerouting

Open Source and Commercial AIOps Platforms

• Surveying available tools: Prometheus, Grafana, ELK, Moogsoft, Dynatrace
• Establishing evaluation criteria for selecting an AIOps platform
• Conducting demonstrations and hands-on activities with a selected stack

Summary and Next Steps