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

Introduction to AIOps

• Defining AIOps and its significance in modern IT
• Comparing traditional monitoring with AIOps-driven observability
• Key components and architecture of AIOps systems

Collection and Normalization of Operational Data

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

Data Correlation and Anomaly Detection

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

Alerting and Noise Reduction

• Crafting intelligent alert rules and appropriate thresholds
• Implementing suppression, deduplication, and alert grouping strategies
• Integration with Alertmanager, Slack, PagerDuty, or Opsgenie

Root Cause Analysis and Visualization

• Leveraging dashboards to visualize metrics and identify trends
• Analyzing events and timelines to support Root Cause Analysis (RCA)
• Tracking issues across layers using distributed tracing tools

Automation and Remediation

• Initiating automated scripts or workflows triggered by incidents
• Connecting with ITSM systems like ServiceNow and Jira
• Key use cases: self-healing, auto-scaling, and traffic rerouting

Open Source and Commercial AIOps Platforms

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

Summary and Next Steps