Building an AIOps Pipeline with Open Source Tools Training Course

Architecting an Open AIOps Framework

• Overview of critical components in open AIOps pipelines
• Data journey from ingestion to alerting
• Evaluating tools and defining integration strategies

Data Ingestion and Aggregation

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

Creating Observability Dashboards

• Visualizing metrics with Grafana
• Developing Kibana dashboards for log analysis
• Extracting operational insights through Elasticsearch queries

Anomaly Detection and Incident Forecasting

• Routing observability data into Python pipelines
• Training ML models for outlier identification and prediction
• Implementing models for live inference within the observability stack

Alerting and Automation via Open Tools

• 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 Strategies

• Managing high-volume ingestion and long-term data retention
• Implementing security and access controls in open-source environments
• Scaling individual layers: ingestion, processing, and alerting

Real-World Applications and Extensions

• Case studies: performance optimization, downtime avoidance, and cost efficiency
• Enhancing pipelines with tracing tools or service graphs
• Best practices for operationalizing and maintaining AIOps in production

Summary and Future Directions