Building an AIOps Pipeline with Open Source Tools Training Course

Architecting an Open-Source AIOps Framework

• Overview of essential components in open AIOps pipelines
• Data progression from ingestion to alerting
• Tool evaluation and integration strategies

Data Acquisition and Aggregation

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

Developing Observability Dashboards

• Visualizing metrics with Grafana
• Constructing Kibana dashboards for log analysis
• Leveraging Elasticsearch queries to derive operational insights

Anomaly Identification and Incident Forecasting

• Exporting observability data into Python workflows
• 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 automated responses
• Utilizing open-source orchestration tools (e.g., Ansible, Rundeck)

Integration and Scalability Factors

• Managing high-volume ingestion and long-term retention
• Security and access control within open-source stacks
• Scaling individual layers independently: ingestion, processing, alerting

Real-World Use Cases and Extensions

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

Recap and Future Directions