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

Introduction to AIOps

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

Gathering and Standardizing Operational Data

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

Correlating Data and Detecting Anomalies

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

Optimizing Alerting and Reducing Noise

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

Performing Root Cause Analysis and Visualization

• Using dashboards to visualize metrics and identify trends
• Reviewing events and timelines to support RCA
• Tracking issues across layers using distributed tracing tools

Automating Processes 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

Surveying Open Source and Commercial AIOps Platforms

• Reviewing key tools: Prometheus, Grafana, ELK, Moogsoft, and Dynatrace
• Establishing evaluation criteria for selecting an AIOps platform
• Conducting demos and hands-on activities with a chosen stack

Recap and Future Directions