Building an AIOps Pipeline with Open Source Tools Training Course

Architecting an Open-Source AIOps Framework

• Examination of essential elements in open-source AIOps pipelines
• Mapping the data journey from ingestion to alerting
• Comparative analysis of tools and integration strategies

Data Acquisition and Aggregation

• Ingesting time-series data via Prometheus
• Logging capture using Logstash and Beats
• Standardizing data for effective cross-source correlation

Developing Observability Dashboards

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

Anomaly Detection and Incident Forecasting

• Transferring observability data into Python processing pipelines
• Training ML models for outlier identification and predictive analysis
• Deploying models for real-time inference within the observability stack

Alerting and Automation with Open-Source Tools

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

Integration and Scalability Factors

• Managing high-volume data ingestion and long-term storage
• Implementing security measures and access controls in open-source stacks
• Independent scaling of layers: ingestion, processing, and alerting

Practical Applications and Extensions

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

Recap and Future Directions