Building an AIOps Pipeline with Open Source Tools Training Course

Designing an Open AIOps Architecture

• An overview of critical components within open AIOps pipelines
• Mapping data flow from ingestion to alerting
• Evaluating tools and defining integration strategies

Data Collection and Aggregation

• Ingesting time-series data via Prometheus
• Capturing logs using Logstash and Beats
• Standardising data for cross-source correlation

Building Observability Dashboards

• Visualising metrics with Grafana
• Creating Kibana dashboards for log analytics
• Leveraging Elasticsearch queries to extract operational insights

Anomaly Detection and Incident Prediction

• Exporting observability data into Python pipelines
• Training ML models for outlier detection and forecasting
• Deploying models for live inference within the observability pipeline

Alerting and Automation with Open Tools

• Configuring Prometheus alert rules and Alertmanager routing
• Triggering scripts or API workflows for automated response
• Utilising open-source orchestration tools (e.g., Ansible, Rundeck)

Integration and Scalability Considerations

• Managing high-volume ingestion and long-term 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 covering performance tuning, downtime prevention, and cost optimisation
• Expanding pipelines with tracing tools or service graphs
• Best practices for operating and maintaining AIOps in production

Summary and Next Steps