Building an AIOps Pipeline with Open Source Tools Training Course

Architecting an Open AIOps Framework

• Introduction to the primary components of open AIOps pipelines
• Data journey from initial ingestion to final alerting
• Comparative analysis of tools and integration strategies

Data Gathering and Aggregation

• Acquiring time-series data via Prometheus
• Capturing log data using Logstash and Beats
• Standardizing data to enable correlation across multiple sources

Developing Observability Dashboards

• Displaying metrics through 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 machine learning models for detecting outliers and making predictions
• Deploying models for live inference within the observability pipeline

Alerting and Automation via Open Source Tools

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

Integration and Scalability Factors

• Managing high-volume data ingestion and long-term storage
• Implementing security and access controls within open-source stacks
• Scaling individual layers independently: ingestion, processing, and alerting

Practical Applications and Extensions

• Case studies covering performance optimization, preventing downtime, and reducing costs
• Expanding pipelines with tracing utilities or service graphs
• Best practices for operating and maintaining AIOps systems in production

Recap and Future Directions