Building an AIOps Pipeline with Open Source Tools Training Course

Designing an Open AIOps Architecture

• Examining the primary components of open AIOps pipelines.
• Mapping the data journey from ingestion to alert generation.
• Evaluating tool compatibility and integration strategies.

Data Collection and Aggregation

• Ingesting time-series data via Prometheus.
• Capturing log data using Logstash and Beats.
• Standardizing data to enable correlation across multiple sources.

Building Observability Dashboards

• Visualizing performance metrics in Grafana.
• Constructing Kibana dashboards for detailed log analysis.
• Utilizing Elasticsearch queries to derive operational insights.

Anomaly Detection and Incident Prediction

• Exporting observability data for processing in Python pipelines.
• Training machine learning models for outlier identification and forecasting.
• Integrating models for live inference within the observability stack.

Alerting and Automation with Open Tools

• Defining Prometheus alert rules and configuring Alertmanager routing.
• Initiating scripts or API workflows for automated response.
• Employing open-source orchestration tools such as Ansible and Rundeck.

Integration and Scalability Considerations

• Managing high-volume data ingestion and long-term storage.
• Implementing security and access controls within open-source stacks.
• Scaling individual layers independently, including ingestion, processing, and alerting.

Real-World Applications and Extensions

• Case studies focusing on performance optimization, downtime mitigation, and cost efficiency.
• Enhancing pipelines with tracing tools or service mapping.
• Best practices for operating and maintaining AIOps in production settings.

Conclusion and Future Steps