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Course Outline
Designing an Open AIOps Architecture
- Overview of key components in open AIOps pipelines
- Data flow from ingestion to alerting
- Tool comparison and integration strategy
Data Collection and Aggregation
- Ingesting time-series data with Prometheus
- Capturing logs with Logstash and Beats
- Normalizing data for cross-source correlation
Building Observability Dashboards
- Visualizing metrics with Grafana
- Building Kibana dashboards for log analytics
- Using Elasticsearch queries to extract operational insights
Anomaly Detection and Incident Prediction
- Exporting observability data to Python pipelines
- Training ML models for outlier detection and forecasting
- Deploying models for live inference in the observability pipeline
Alerting and Automation with Open Tools
- Creating Prometheus alert rules and Alertmanager routing
- Triggering scripts or API workflows for auto-response
- Using open-source orchestration tools (e.g., Ansible, Rundeck)
Integration and Scalability Considerations
- Handling high-volume ingestion and long-term retention
- Security and access control in open-source stacks
- Scaling each layer independently: ingestion, processing, alerting
Real-World Applications and Extensions
- Case studies: performance tuning, downtime prevention, and cost optimization
- Extending pipelines with tracing tools or service graphs
- Best practices for running and maintaining AIOps in production
Summary and Next Steps
Requirements
- Experience with observability tools such as Prometheus or ELK
- Working knowledge of Python and machine learning fundamentals
- Understanding of IT operations and alerting workflows
Audience
- Advanced site reliability engineers (SREs)
- Data engineers working in operations
- DevOps platform leads and infrastructure architects
14 Hours
