Building an AIOps Pipeline with Open Source Tools Training Course

Designing an Open AIOps Framework

• Survey of essential elements in open AIOps workflows
• Data trajectory from ingestion to notification
• Evaluation of tools and integration methodologies

Data Acquisition and Aggregation

• Collecting time-series data via Prometheus
• Log capture using Logstash and Beats
• Data standardization for cross-source analysis

Developing Observability Dashboards

• Metrics visualization using Grafana
• Creating Kibana interfaces for log analysis
• Leveraging Elasticsearch queries for operational intelligence

Anomaly Identification and Incident Forecasting

• Channeling observability data into Python workflows
• Training ML models for outlier detection and prediction
• Model deployment for real-time inference within the observability stack

Alerting and Automation via Open Technologies

• Defining Prometheus alert rules and 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 ingestion and long-term storage
• Security and access management in open-source environments
• Independent scaling of layers: ingestion, processing, and alerting

Practical Applications and Extensions

• Case studies: performance optimization, uptime assurance, and cost reduction
• Enhancing pipelines with tracing utilities or service mapping
• Best practices for operationalizing AIOps in production

Recap and Future Directions