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Course Outline
Introduction to AIOps
- What is AIOps and why it matters.
- Traditional monitoring versus AIOps-driven observability.
- AIOps architecture and key components.
Collecting and Normalising Operational Data
- Types of observability data: metrics, logs, and traces.
- Ingesting data from multiple sources (servers, containers, cloud).
- Using agents and exporters (Prometheus, Beats, Fluentd).
Data Correlation and Anomaly Detection
- Time series correlation and statistical methods.
- Using machine learning models for anomaly detection.
- Detecting incidents across distributed systems.
Alerting and Noise Reduction
- Designing intelligent alert rules and thresholds.
- Suppression, deduplication, and alert grouping.
- Integration with Alertmanager, Slack, PagerDuty, or Opsgenie.
Root Cause Analysis and Visualisation
- Using dashboards to visualise metrics and detect trends.
- Exploring events and timelines for root cause analysis (RCA).
- Tracing issues across layers with distributed tracing tools.
Automation and Remediation
- Triggering automated scripts or workflows from incidents.
- Integration 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.
- Evaluation criteria for selecting an AIOps platform.
- Demo and hands-on with a selected stack.
Summary and Next Steps
Requirements
- A solid understanding of IT operations and system monitoring concepts.
- 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
