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Course Outline

Overview of AI-Driven Operations

  • Definition and strategic importance of AIOps for government operations
  • Comparison of traditional monitoring frameworks with AIOps-enabled observability
  • Structural components and architectural foundations of AIOps systems

Aggregation and Standardization of Operational Data

  • Categorization of observability data types, including metrics, logs, and traces
  • Ingestion mechanisms for heterogeneous sources (infrastructure servers, containers, cloud environments)
  • Deployment and management of telemetry agents and exporters (e.g., Prometheus, Beats, Fluentd)

Data Correlation and Anomaly Identification

  • Application of time series correlation techniques and statistical analysis
  • Implementation of machine learning models for automated anomaly detection
  • Identification of system incidents within complex, distributed environments

Alert Management and Noise Mitigation

  • Development of intelligent alerting rules and dynamic threshold configurations
  • Implementation of suppression, deduplication, and grouping protocols to reduce redundancy
  • Integration with notification platforms such as Alertmanager, Slack, PagerDuty, or Opsgenie for streamlined communication

Root Cause Investigation and Data Visualization

  • Utilization of dashboard interfaces to monitor metrics and identify performance trends
  • Analysis of event histories and temporal timelines to support root cause analysis (RCA)
  • Tracing operational issues across system layers using distributed tracing methodologies

Process Automation and Remediation

  • Automated execution of scripts and workflows triggered by incident detection
  • Interoperability with IT service management (ITSM) platforms, including ServiceNow and Jira
  • Operational use cases: self-healing capabilities, elastic scaling, and dynamic traffic routing

Evaluation of Open Source and Commercial AIOps Solutions

  • Review of established tools such as Prometheus, Grafana, ELK Stack, Moogsoft, and Dynatrace for government use cases
  • Establishment of criteria for the procurement and selection of AIOps platforms
  • Demonstration and practical application of a selected technical stack

Conclusion and Strategic Next Steps

Requirements

  • Comprehension of information technology operations and system surveillance principles
  • Proficiency in utilizing monitoring platforms and data visualization dashboards
  • Knowledge of standard logging and telemetry data structures

Target Stakeholders

  • Operational units tasked with infrastructure and application management
  • Site Reliability Engineering personnel
  • Teams dedicated to IT monitoring and observability solutions for government entities
 14 Hours

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