Building an AIOps Pipeline with Open Source Tools Training Course

Architecting an Open-Source AIOps Framework

• Overview of essential elements in open AIOps workflows
• Data progression from initial capture to alert delivery
• Comparative analysis of tools and integration methodologies

Data Acquisition and Consolidation

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

Constructing Observability Interfaces

• Displaying metrics through Grafana
• Developing Kibana dashboards for log analysis
• Extracting operational insights using Elasticsearch queries

Anomaly Identification and Incident Forecasting

• Transferring observability data into Python environments
• Training ML models for outlier identification and trend prediction
• Implementing models for real-time inference within the pipeline

Alerting and Automation via Open-Source Solutions

• 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 data intake and long-term storage
• Security protocols and access controls within open-source ecosystems
• Independently scaling ingestion, processing, and alerting layers

Practical Applications and Future Extensions

• Case studies covering performance optimization, outage avoidance, and cost reduction
• Expanding pipelines with tracing utilities or service mapping tools
• Best practices for operating and sustaining AIOps in production

Recap and Future Directions