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Course Outline
Designing an Open AIOps Architecture
- Overview of essential components within open AIOps pipelines
- Data flow progression from ingestion to alerting
- Tool comparison and integration strategy
Data Collection and Aggregation
- Ingesting time-series data via Prometheus
- Capturing logs using Logstash and Beats
- Normalizing data for cross-source correlation
Developing Observability Dashboards
- Visualizing metrics with Grafana
- Creating Kibana dashboards for log analytics
- Leveraging Elasticsearch queries to derive operational insights
Anomaly Detection and Incident Prediction
- Exporting observability data to Python pipelines
- Training ML models for outlier detection and forecasting
- Deploying models for real-time inference within the observability pipeline
Alerting and Automation with Open Tools
- Defining Prometheus alert rules and configuring Alertmanager routing
- Triggering scripts or API workflows for automated responses
- Utilizing open-source orchestration tools (e.g., Ansible, Rundeck)
Integration and Scalability Considerations
- Managing high-volume ingestion and long-term data retention
- Ensuring security and access control within open-source stacks
- Scaling individual layers independently: ingestion, processing, and 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 operating and maintaining AIOps in production environments
Summary and Next Steps
Requirements
- Prior experience with observability tools such as Prometheus or ELK
- Practical knowledge of Python and core machine learning concepts
- Familiarity with IT operations and alerting workflows
Target Audience
- Advanced Site Reliability Engineers (SREs)
- Data engineers focused on operational tasks
- DevOps platform leads and infrastructure architects
14 Hours
