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

Introduction to AIOps

• Defining AIOps and its strategic importance
• Comparing traditional monitoring with AIOps-driven observability
• Examining AIOps architecture and essential components

Gathering and Standardizing Operational Data

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

Data Correlation and Anomaly Identification

• Applying time series correlation and statistical techniques
• Deploying ML models for anomaly detection
• Identifying incidents across distributed system architectures

Intelligent Alerting and Noise Mitigation

• Crafting smart alert rules and 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 uncover trends
• Analyzing events and timelines for comprehensive RCA
• Tracking issues across system layers using distributed tracing tools

Automation and Remediation

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

Open-Source and Commercial AIOps Solutions

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

Recap and Future Directions