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Course Outline
Designing an Open AIOps Architecture
- Examination of core components within open-source AIOps frameworks
- Mechanisms for data transmission from collection to alert generation
- Evaluation of tools and strategies for system integration, tailored for government environments
Data Collection and Aggregation
- Utilization of Prometheus for the ingestion of time-series telemetry data
- Log acquisition processes using Logstash and Beats infrastructure
- Standardization of data formats to facilitate correlation across disparate sources
Building Observability Dashboards
- Metric visualization capabilities through Grafana interfaces
- Development of log analytics dashboards using Kibana
- Application of Elasticsearch queries to derive operational intelligence
Anomaly Detection and Incident Prediction
- Transmission of observability data to Python-based analytical pipelines
- Implementation of machine learning models for outlier identification and predictive forecasting
- Deployment of trained models to support real-time inference within the observability workflow
Alerting and Automation with Open Tools
- Configuration of Prometheus alert rules and Alertmanager notification routing
- Execution of automated response mechanisms via scripts and API integrations
- Deployment of open-source orchestration platforms, such as Ansible and Rundeck
Integration and Scalability Considerations
- Management of high-volume data ingestion and extended retention policies
- Implementation of security protocols and access control measures within open-source stacks for government use
- Independent scaling of system layers, including ingestion, processing, and alerting components
Real-World Applications and Extensions
- Analysis of case studies focusing on performance optimization, outage mitigation, and fiscal responsibility
- Expansion of pipeline functionality through distributed tracing and service dependency mapping
- Establishment of operational best practices for the sustained management of AIOps in production environments
Summary and Next Steps
Requirements
- Proficiency in utilizing observability platforms, including Prometheus or ELK stack
- Practical experience with Python programming and core machine learning principles
- Comprehensive understanding of information technology operations and incident alerting procedures
Audience
- Senior site reliability engineers (SREs)
- Data engineers supporting operational functions
- Leads for DevOps platforms and infrastructure architects
14 Hours