Building an AIOps Pipeline with Open Source Tools Training Course

Architecting an Open-Source AIOps Framework

• Examination of essential elements in open AIOps workflows
• Tracing data flow from ingestion to alert dispatch
• Comparing tools and defining integration strategies

Data Acquisition and Consolidation

• Processing time-series data via Prometheus
• Capturing log data using Logstash and Beats
• Standardizing data for cross-source correlation

Creating Observability Dashboards

• Displaying metrics using Grafana
• Developing Kibana dashboards for log analysis
• Extracting operational insights through Elasticsearch queries

Anomaly Identification and Incident Forecasting

• Routing observability data into Python pipelines
• Training ML models for outlier detection and prediction
• Deploying models for real-time inference within the observability stack

Alerting and Automation via Open-Source Tools

• Defining Prometheus alert rules and Alertmanager routing
• Initiating scripts or API workflows for automated response
• Utilizing open-source orchestration platforms (e.g., Ansible, Rundeck)

Integration and Scalability Factors

• Managing high-volume ingestion and long-term data retention
• Implementing security and access control within open-source stacks
• Scaling individual layers: ingestion, processing, and alerting

Practical Applications and Extensions

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

Conclusion and Future Directions