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

Introduction to AIOps

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

Collecting and Normalizing Operational Data

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

Data Correlation and Anomaly Detection

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

Alerting and Noise Reduction

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

Root Cause Analysis and Visualization

• Visualizing metrics and detecting trends using dashboards.
• Exploring events and timelines for thorough Root Cause Analysis (RCA).
• Tracing issues across layers with distributed tracing tools.

Automation and Remediation

• Triggering automated scripts or workflows based on incident data.
• Integrating with ITSM systems such as ServiceNow or Jira.
• Exploring use cases like self-healing, automatic scaling, and traffic rerouting.

Open Source and Commercial AIOps Platforms

• Overview of key tools: Prometheus, Grafana, ELK, Moogsoft, and Dynatrace.
• Establishing evaluation criteria for selecting the right AIOps platform.
• Demonstrating and practicing with a selected technology stack.

Summary and Next Steps