Building an AIOps Pipeline with Open Source Tools Training Course

Architecting an Open AIOps Framework

• Survey of essential elements in open AIOps pipelines
• Data pathways from ingestion through to alerting
• Evaluation of tools and integration strategies

Data Acquisition and Consolidation

• Gathering time-series data via Prometheus
• Logging data collection using Logstash and Beats
• Standardizing data for cross-source correlation

Creating Observability Dashboards

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

Anomaly Identification and Incident Forecasting

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

Alerting and Automation via Open-Source 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 data retention
• Implementing security and access controls in open-source stacks
• Scaling individual layers independently: ingestion, processing, and alerting

Practical Applications 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

Conclusion and Future Directions