Building an AIOps Pipeline with Open Source Tools Training Course

Architecting an Open AIOps Framework

• Survey of essential components in open AIOps pipelines
• Data pathway from ingestion to alerting
• Tool evaluation and integration approach

Data Acquisition and Aggregation

• Collection of time-series data via Prometheus
• Log capture using Logstash and Beats
• Standardization of data for cross-source correlation

Creating Observability Dashboards

• Visualization of metrics through Grafana
• Development of Kibana dashboards for log analysis
• Leveraging Elasticsearch queries to derive operational insights

Anomaly Detection and Incident Forecasting

• Exporting observability data to Python pipelines
• Training ML models for outlier identification and forecasting
• Deploying models for real-time inference within the observability stack

Alerting and Automation via Open Tools

• Configuration of Prometheus alert rules and Alertmanager routing
• Activation of scripts or API workflows for automated response
• Utilization of open-source orchestration platforms (e.g., Ansible, Rundeck)

Integration and Scalability Factors

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

Practical Applications and Extensions

• Case studies: performance tuning, downtime prevention, and cost efficiency
• Expanding pipelines with tracing utilities or service maps
• Best practices for operating and sustaining AIOps in production

Conclusion and Future Directions