Building an AIOps Pipeline with Open Source Tools Training Course

Designing an Open AIOps Framework

• Summary of essential elements in open AIOps pipelines
• Data movement from ingestion to alerting stages
• Evaluation of tools and integration approaches

Data Acquisition and Consolidation

• Capturing time-series data via Prometheus
• Collecting logs using Logstash and Beats
• Standardizing data for multi-source correlation

Creating Observability Dashboards

• Displaying metrics using Grafana
• Developing Kibana dashboards for log analysis
• Utilizing Elasticsearch queries to derive operational insights

Anomaly Identification and Incident Forecasting

• Transferring observability data to Python pipelines
• Training ML models for outlier detection and prediction
• Deploying models for real-time inference within the observability pipeline

Alerting and Automation via Open Tools

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

Integration and Scalability Factors

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

Practical Applications and Extensions

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

Wrap-up and Future Directions