Building an AIOps Pipeline with Open Source Tools Training Course

Designing an Open AIOps Architecture

• Key components within open-source AIOps pipelines
• Data journey from ingestion through to alerting
• Comparative analysis of tools and integration strategies

Data Collection and Aggregation

• Ingesting time-series data via Prometheus
• Log capture using Logstash and Beats
• Data normalisation for effective cross-source correlation

Constructing Observability Dashboards

• Visualising metrics with Grafana
• Developing Kibana dashboards for log analysis
• Utilising 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 live inference within the observability stack

Alerting and Automation with Open Tools

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

Integration and Scalability Considerations

• Managing high-volume ingestion and long-term data retention
• Security and access control within open-source stacks
• Independent scaling of ingestion, processing, and alerting layers

Real-World Applications and Extensions

• Case studies: performance tuning, preventing downtime, and cost optimisation
• Enhancing pipelines with tracing tools or service maps
• Best practices for operationalising and maintaining AIOps in production

Summary and Next Steps