Building an AIOps Pipeline with Open Source Tools Training Course

Architecting an Open AIOps Framework

• Introduction to essential elements in open AIOps pipelines
• Data progression from ingestion stages to alert generation
• Evaluating tools and defining integration approaches

Data Acquisition and Consolidation

• Processing time-series data via Prometheus
• Recording logs using Logstash and Beats
• Standardizing data to facilitate multi-source correlation

Developing Observability Interfaces

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

Detecting Anomalies and Forecasting Incidents

• Moving observability data into Python workflows
• Training ML models for outlier recognition and predictive forecasting
• Implementing models for real-time inference within the observability chain

Automation and Alerting with Open-Source Tools

• Establishing Prometheus alert criteria and Alertmanager routing strategies
• Activating scripts or API flows for automated responses
• Utilizing open-source orchestration platforms (such as Ansible, Rundeck)

Integration and Scalability Factors

• Managing high-volume data intake and long-term storage
• Ensuring security and access governance in open-source ecosystems
• Scaling individual layers independently: ingestion, processing, and alerting

Practical Applications and Extensions

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

Recap and Future Directions