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Course Outline
Introduction to AIOps
- Defining AIOps and its significance
- Traditional monitoring compared to AIOps-driven observability
- AIOps architecture and essential components
Collecting and Normalizing Operational Data
- Types of observability data: metrics, logs, and traces
- Ingesting data from diverse sources (servers, containers, cloud)
- Utilizing agents and exporters (Prometheus, Beats, Fluentd)
Data Correlation and Anomaly Detection
- Time series correlation and statistical approaches
- Employing ML models for anomaly detection
- Identifying incidents across distributed systems
Alerting and Noise Reduction
- Designing intelligent alert rules and thresholds
- Suppression, deduplication, and alert grouping strategies
- Integrating with Alertmanager, Slack, PagerDuty, or Opsgenie
Root Cause Analysis and Visualization
- Using dashboards to visualize metrics and identify trends
- Exploring events and timelines for RCA (Root Cause Analysis)
- Tracing issues across layers with 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
- Criteria for evaluating and selecting an AIOps platform
- Demo and hands-on practice with a selected stack
Summary and Next Steps
Requirements
- A solid understanding of IT operations and system monitoring concepts
- Prior experience with monitoring tools or dashboards
- Familiarity with basic log and metric formats
Audience
- Operations teams responsible for infrastructure and applications
- Site Reliability Engineers (SREs)
- IT monitoring and observability teams
14 Hours
