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 key components.

Collecting and Normalizing Operational Data

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

Data Correlation and Anomaly Detection

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

Alerting and Noise Reduction

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

Root Cause Analysis and Visualization

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

Automation and Remediation

• Initiating automated scripts or workflows triggered by incidents.
• Connecting with ITSM systems (ServiceNow, Jira).
• Application examples: self-healing, scaling, and traffic rerouting.

Open Source and Commercial AIOps Platforms

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

Summary and Next Steps