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

Introduction to AIOps

• Defining AIOps and its significance in modern operations
• Comparing traditional monitoring with AIOps-driven observability
• Architecture of AIOps and its essential components

Collection and Normalization of Operational Data

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

Data Correlation and Anomaly Detection

• Time series correlation and associated statistical methods
• Applying ML models for effective anomaly detection
• Identifying incidents within distributed system environments

Alerting Strategies and Noise Reduction

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

Root Cause Analysis and Visualization

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

Automation and Remediation Processes

• Initiating automated scripts or workflows triggered by incidents
• Integration with ITSM platforms such as ServiceNow and Jira
• Use cases including 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 appropriate AIOps platform
• Live demonstration and hands-on practice with a selected stack

Summary and Recommended Next Steps