Building an AIOps Pipeline with Open Source Tools Training Course

Architecting an Open AIOps Framework

• Review of essential components in open AIOps pipelines
• Mapping the data journey from ingestion to alerting
• Evaluating tools and defining integration strategies

Data Collection and Aggregation

• Acquiring time-series data via Prometheus
• Recording logs using Logstash and Beats
• Standardizing data to enable cross-source correlation

Developing Observability Dashboards

• Presenting metrics through Grafana
• Assembling Kibana dashboards for log analysis
• Extracting operational insights via Elasticsearch queries

Anomaly Detection and Incident Forecasting

• Integrating observability data into Python workflows
• Training ML models for outlier identification and prediction
• Deploying models for real-time inference within the observability stack

Alerting and Automation Using Open Tools

• Defining Prometheus alert rules and configuring Alertmanager routing
• Activating scripts or API workflows for automated responses
• 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 controls in open-source stacks
• Scalable architecture: independent scaling of ingestion, processing, and alerting layers

Practical Applications and Extensions

• Case studies: performance tuning, preventing downtime, and cost efficiency
• Enhancing pipelines with tracing utilities or service graphs
• Best practices for operating and sustaining AIOps in production

Summary and Future Directions