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

Introduction to AIOps

• Defining AIOps and its significance in modern IT
• Comparing traditional monitoring with AIOps-driven observability
• Examining AIOps architecture and its essential components

Collecting and Normalizing Operational Data

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

Data Correlation and Anomaly Detection

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

Alerting and Noise Reduction

• Crafting intelligent alert rules and thresholds
• Implementing suppression, deduplication, and alert grouping
• Integrating with platforms like Alertmanager, Slack, PagerDuty, or Opsgenie

Root Cause Analysis and Visualization

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

Automation and Remediation

• Initiating automated scripts or workflows triggered by incidents
• Connecting with ITSM systems such as ServiceNow and Jira
• Exploring use cases including self-healing, scaling, and traffic rerouting

Open Source and Commercial AIOps Platforms

• Reviewing key tools: Prometheus, Grafana, ELK, Moogsoft, and Dynatrace
• Defining evaluation criteria for selecting an AIOps platform
• Demonstrating and practicing with a chosen stack

Summary and Next Steps