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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