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 Duration 14 hours

Course Outline

Introduction to AIOps

  • Definition of AIOps and its strategic importance for government
  • Comparative analysis: conventional monitoring versus AIOps-driven observability
  • AIOps architectural frameworks and core components

Collection and Standardization of Operational Data

  • Categorization of observability data: metrics, logs, and distributed traces
  • Aggregation of data from heterogeneous sources (servers, containers, cloud environments)
  • Deployment of agents and exporters (e.g., Prometheus, Beats, Fluentd)

Data Correlation and Anomaly Identification

  • Time-series correlation techniques and statistical methodologies
  • Application of machine learning models for anomaly detection
  • Identification of incidents spanning distributed system architectures

Alerting Optimization and Noise Mitigation

  • Development of intelligent alerting rules and dynamic thresholds
  • Implementation of suppression, deduplication, and alert grouping strategies
  • Integration with notification platforms such as Alertmanager, Slack, PagerDuty, or Opsgenie

Root Cause Analysis and Visual Analytics

  • Utilization of dashboards for metric visualization and trend analysis
  • Examination of event timelines to facilitate root cause analysis
  • Cross-layer issue tracing using distributed tracing tools

Automation and Remediation Workflows

  • Initiation of automated scripts or workflows in response to incidents
  • Integration with IT Service Management systems (e.g., ServiceNow, Jira)
  • Operational use cases: self-healing, resource scaling, and traffic rerouting

Open Source and Commercial AIOps Ecosystems

  • Assessment of leading tools: Prometheus, Grafana, ELK, Moogsoft, and Dynatrace
  • Evaluation criteria for selecting AIOps platforms suitable for government deployment
  • Demonstration and practical application of selected technology stacks

Summary and Implementation Roadmap

Requirements

  • Foundational understanding of IT operations and system monitoring principles
  • Practical experience with monitoring tools and dashboard interfaces
  • Familiarity with standard log and metric data formats

Target Audience

  • Operations teams accountable for infrastructure and application management
  • Site Reliability Engineers (SREs)
  • IT monitoring and observability specialized teams

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