Building an AIOps Pipeline with Open Source Tools Training Course

Designing an Open AIOps Architecture

• Key components of open AIOps pipelines
• Data progression from intake to alerting
• Tool evaluation and integration strategies

Data Collection and Aggregation

• Capturing time-series data via Prometheus
• Logging with Logstash and Beats
• Standardizing data for cross-source analysis

Developing Observability Dashboards

• Displaying metrics through Grafana
• Creating Kibana dashboards for log analysis
• Leveraging Elasticsearch queries for operational insights

Anomaly Detection and Incident Forecasting

• Transferring observability data to Python workflows
• Training ML models for outlier identification and predictions
• Deploying models for real-time inference within the observability stack

Alerting and Automation via Open Tools

• Configuring Prometheus alert rules and Alertmanager routing
• Initiating scripts or API workflows for automated responses
• Employing open-source orchestration tools (such as Ansible, Rundeck)

Integration and Scalability Factors

• Managing high-volume ingestion and long-term data retention
• Security and access management in open-source environments
• Independent scaling of layers: ingestion, processing, and alerting

Practical Applications and Extensions

• Case studies: performance optimization, uptime assurance, and cost reduction
• Expanding pipelines with tracing utilities or service maps
• Best practices for operating and sustaining AIOps in production

Conclusion and Future Pathways