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Course Outline
Introduction to AIOps
- Defining AIOps and its significance
- Comparing traditional monitoring with AIOps-driven observability
- Overview of AIOps architecture and essential components
Collecting and Normalizing Operational Data
- Categories of observability data: metrics, logs, and traces
- Ingesting data from diverse sources (servers, containers, cloud)
- Leveraging agents and exporters (Prometheus, Beats, Fluentd)
Data Correlation and Anomaly Detection
- Time series correlation and statistical approaches
- Applying ML models for anomaly detection
- Identifying incidents across distributed systems
Alerting and Noise Reduction
- Crafting intelligent alert rules and setting thresholds
- Techniques for 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
- Investigating events and timelines for RCA
- Tracing issues across layers using distributed tracing tools
Automation and Remediation
- Initiating automated scripts or workflows triggered by incidents
- Connecting with ITSM systems (ServiceNow, Jira)
- Use cases: self-healing, scaling, and traffic rerouting
Open Source and Commercial AIOps Platforms
- Overview of tools: Prometheus, Grafana, ELK, Moogsoft, Dynatrace
- Criteria for selecting the right AIOps platform
- Demo and hands-on practice with a chosen stack
Summary and Next Steps
Requirements
- A solid understanding of IT operations and system monitoring concepts
- Practical experience with monitoring tools or dashboards
- Familiarity with fundamental log and metric formats
Audience
- Operations teams managing infrastructure and applications
- Site Reliability Engineers (SREs)
- IT monitoring and observability teams
14 Hours
