Building an AIOps Pipeline with Open Source Tools Training Course

Designing an Open AIOps Framework

• Key elements of open AIOps pipelines
• Data pathways from ingestion to alert generation
• Comparative analysis of tools and integration strategies

Data Acquisition and Consolidation

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

Developing Observability Dashboards

• Metric visualization with Grafana
• Creating Kibana dashboards for log analysis
• Utilizing Elasticsearch queries to derive operational insights

Anomaly Identification and Incident Forecasting

• Transferring observability data to Python workflows
• Training machine learning models for outlier detection and forecasting
• Deploying models for real-time inference within the observability stack

Alerting and Automation via Open Tools

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

Integration and Scalability Strategies

• Managing high-volume ingestion and long-term data retention
• Security protocols and access control in open-source environments
• Independently scaling ingestion, processing, and alerting layers

Practical Applications and Expansions

• Case studies covering performance tuning, outage prevention, and cost efficiency
• Augmenting pipelines with tracing tools or service mapping
• Best practices for operationalizing and sustaining AIOps in production

Recap and Future Directions