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Duration 14 hours
Course Outline
Introduction to AIOps
- Defining AIOps and understanding its significance
- Comparing traditional monitoring with AIOps-driven observability
- Examining AIOps architecture and its key components
Collecting and Normalizing Operational Data
- Types 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 methods
- Applying ML models for anomaly detection
- Identifying incidents across distributed systems
Alerting and Noise Reduction
- Crafting intelligent alert rules and thresholds
- Implementing suppression, deduplication, and alert grouping
- Integration with Alertmanager, Slack, PagerDuty, or Opsgenie
Root Cause Analysis and Visualization
- Visualizing metrics and spotting trends using dashboards
- Analyzing events and timelines for Root Cause Analysis (RCA)
- Tracing issues across layers with distributed tracing tools
Automation and Remediation
- Initiating automated scripts or workflows triggered by incidents
- Connecting with ITSM systems (ServiceNow, Jira)
- Application scenarios: self-healing, scaling, and traffic rerouting
Open Source and Commercial AIOps Platforms
- Overview of tools: Prometheus, Grafana, ELK, Moogsoft, Dynatrace
- Criteria for evaluating and selecting an AIOps platform
- Live demo and hands-on practice with a chosen stack
Summary and Next Steps
Requirements
- A solid grasp of IT operations and system monitoring principles
- Prior experience with monitoring tools or dashboards
- Knowledge of fundamental log and metric formats
Target Audience
- Operations teams managing infrastructure and applications
- Site Reliability Engineers (SREs)
- IT monitoring and observability teams
