Building an AIOps Pipeline with Open Source Tools Training Course

Designing an Open AIOps Architecture

• Introduction to essential components in open AIOps pipelines
• Data pathway from ingestion to alerting
• Comparison of tools and integration approaches

Data Collection and Aggregation

• Importing time-series data via Prometheus
• Log capture using Logstash and Beats
• Standardizing data for cross-source analysis

Building Observability Dashboards

• Metric visualization with Grafana
• Log analytics dashboards in Kibana
• Extracting operational insights through Elasticsearch queries

Anomaly Detection and Incident Prediction

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

Alerting and Automation with Open Tools

• Setting up Prometheus alert rules and Alertmanager routing
• Activating scripts or API workflows for automatic 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 in open-source stacks
• Independent scaling of layers: ingestion, processing, and alerting

Real-World Applications and Extensions

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

Summary and Next Steps