Building an AIOps Pipeline with Open Source Tools Training Course

Designing an Open-Source AIOps Architecture

• Overview of essential components in open AIOps pipelines
• Data flow from ingestion to alerting
• Tool comparison and integration strategies

Data Collection and Aggregation

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

Building Observability Dashboards

• Visualizing metrics with Grafana
• Creating Kibana dashboards for log analysis
• Utilizing 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 pipeline

Alerting and Automation with Open Tools

• Defining Prometheus alert rules and Alertmanager routing
• Triggering 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 retention
• Implementing security and access control in open-source stacks
• Scaling layers independently: ingestion, processing, alerting

Real-World Applications and Extensions

• Case studies: performance tuning, downtime prevention, and cost optimization
• Expanding pipelines with tracing tools or service graphs
• Best practices for operating and maintaining AIOps in production

Summary and Next Steps