Building an AIOps Pipeline with Open Source Tools Training Course

Architecting an Open AIOps Framework

• Key elements of open AIOps pipelines
• Data movement from intake to notification
• Tool evaluation and integration strategies

Data Acquisition and Consolidation

• Ingesting time-series data via Prometheus
• Logging data capture using Logstash and Beats
• Data standardization for multi-source correlation

Developing Observability Dashboards

• Metric visualization with Grafana
• Creating Kibana dashboards for log analysis
• Extracting operational insights using Elasticsearch queries

Anomaly Identification and Incident Forecasting

• Moving observability data into Python workflows
• Training ML models for outlier detection and trend forecasting
• Implementing models for real-time inference within the observability stack

Notifications and Automation via Open Tools

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

Integration and Scalability Factors

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

Practical Uses and Enhancements

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

Recap and Future Directions