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

Introduction to AIOps

• Defining AIOps and its significance.
• Comparing traditional monitoring with AIOps-driven observability.
• Exploring AIOps architecture and its key components.

Collecting and Normalizing Operational Data

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

Data Correlation and Anomaly Detection

• Time series correlation and statistical methods.
• Applying ML models for anomaly detection.
• Identifying incidents across distributed systems.

Alerting and Noise Reduction

• Designing intelligent alert rules and thresholds.
• Implementing suppression, deduplication, and alert grouping.
• Integrating with Alertmanager, Slack, PagerDuty, or Opsgenie.

Root Cause Analysis and Visualization

• Using dashboards to visualize metrics and identify trends.
• Analyzing events and timelines for RCA.
• Tracing issues across layers using distributed tracing tools.

Automation and Remediation

• Triggering automated scripts or workflows from incidents.
• Integrating with ITSM systems (ServiceNow, Jira).
• Use cases: self-healing, scaling, traffic rerouting.

Open Source and Commercial AIOps Platforms

• Overview of tools: Prometheus, Grafana, ELK, Moogsoft, Dynatrace.
• Evaluation criteria for selecting an AIOps platform.
• Demo and hands-on practice with a selected stack.

Summary and Next Steps