Building an AIOps Pipeline with Open Source Tools Training Course

Architecting an Open AIOps Framework

• Key elements of open AIOps pipelines
• Data pathways from ingestion to alerting
• Comparative analysis of tools and integration strategies

Data Gathering and Aggregation

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

Developing Observability Dashboards

• Visualising metrics using Grafana
• Constructing Kibana dashboards for log analysis
• Utilising Elasticsearch queries to derive operational insights

Anomaly Identification and Incident Forecasting

• Exporting observability data to Python workflows
• Training ML models for outlier identification and forecasting
• Deploying models for live inference within the observability stack

Alerting and Automation with Open-Source Solutions

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

Integration and Scalability Factors

• Managing high-volume ingestion and long-term data retention
• Security and access controls within open-source stacks
• Independently scaling each layer: ingestion, processing, and alerting

Practical Applications and Extensions

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

Recap and Future Directions