Building an AIOps Pipeline with Open Source Tools Training Course

Designing an Open AIOps Architecture

• Key components of open AIOps pipelines
• Data movement from ingestion to alerting
• Comparing tools and defining integration strategies

Data Collection and Aggregation

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

Creating Observability Dashboards

• Metric visualization with Grafana
• Constructing Kibana dashboards for log analysis
• Leveraging Elasticsearch queries for operational insights

Anomaly Detection and Incident Prediction

• Routing observability data into Python pipelines
• Training ML models for outlier identification and forecasting
• Deploying models for real-time inference in the pipeline

Alerting and Automation with Open Tools

• Defining Prometheus alert rules and Alertmanager routing
• Initiating scripts or API workflows for automated responses
• Utilizing open-source orchestration tools (such as Ansible, Rundeck)

Integration and Scalability Considerations

• Managing high-volume ingestion and long-term data retention
• Security and access control within open-source stacks
• Independent scaling of ingestion, processing, and alerting layers

Real-World Applications and Extensions

• Case studies on performance tuning, downtime prevention, and cost efficiency
• Expanding pipelines with tracing tools or service maps
• Best practices for operationalizing and maintaining AIOps in production

Summary and Next Steps