Building an AIOps Pipeline with Open Source Tools Training Course

Designing an Open-Source AIOps Architecture

• Overview of essential components within open AIOps pipelines
• Mapping the data flow from ingestion to alerting
• Comparative analysis of tools and integration strategies

Data Collection and Aggregation

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

Developing Observability Dashboards

• Visualizing metrics through Grafana
• Creating Kibana dashboards for log analytics
• Utilizing Elasticsearch queries to derive operational insights

Anomaly Detection and Incident Forecasting

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

Alerting and Automation with Open Tools

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

Integration and Scalability Considerations

• Managing high-volume ingestion and long-term data retention
• Ensuring security and access control within open-source stacks
• Scaling each layer independently: ingestion, processing, and alerting

Real-World Applications and Extensions

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

Summary and Future Steps