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Duration 14 hours
Course Outline
Introduction to AIOps
- Defining AIOps and its significance
- Conventional oversight versus AIOps-led observability
- AIOps structure and essential elements
Gathering and Standardizing Operational Information
- Kinds of observability data: indicators, logs, and traces
- Importing data from diverse sources (servers, containers, cloud)
- Utilizing agents and exporters (Prometheus, Beats, Fluentd)
Information Linking and Anomaly Identification
- Temporal series linking and statistical approaches
- Applying ML models for spotting anomalies
- Identifying incidents across decentralized systems
Notification Strategies and Clutter Minimization
- Creating smart notification rules and limits
- Muting, removing duplicates, and grouping alerts
- Connecting with Alertmanager, Slack, PagerDuty, or Opsgenie
Root Cause Investigation and Visual Analysis
- Using control panels to display indicators and track patterns
- Analyzing events and timelines for RCA
- Tracking problems across layers using distributed tracing utilities
Automation and Resolution
- Activating automated scripts or workflows from incidents
- Integrating with ITSM platforms (ServiceNow, Jira)
- Application examples: self-healing, scaling, traffic redirection
Open Source and Commercial AIOps Solutions
- Overview of tools: Prometheus, Grafana, ELK, Moogsoft, Dynatrace
- Assessment criteria for choosing an AIOps platform
- Demonstration and practical work with a chosen stack
Recap and Future Directions
Requirements
- A solid grasp of IT operations and system oversight principles
- Practical experience with oversight instruments or control panels
- Basic knowledge of standard log and indicator structures
Target Audience
- Operational teams managing infrastructure and applications
- Site Reliability Engineers (SREs)
- IT oversight and observability specialists
