Building an AIOps Pipeline with Open Source Tools Training Course

Designing an Open Source AIOps Architecture

• An overview of the critical components within open AIOps pipelines.
• Mapping the data journey from initial ingestion to final alerting.
• Comparing available tools and defining an integration strategy.

Data Collection and Aggregation

• Ingesting time-series data using Prometheus.
• Capturing log data with Logstash and Beats.
• Normalizing datasets to facilitate cross-source correlation.

Building Observability Dashboards

• Visualizing key metrics with Grafana.
• Developing Kibana dashboards for advanced log analytics.
• Leveraging Elasticsearch queries to derive actionable operational insights.

Anomaly Detection and Incident Prediction

• Exporting observability data into Python-based processing pipelines.
• Training machine learning models for outlier detection and trend forecasting.
• Deploying trained models for live inference within the observability stack.

Alerting and Automation with Open Tools

• Defining Prometheus alert rules and configuring Alertmanager routing.
• Triggering scripts or API workflows to enable automated response.
• Utilizing open-source orchestration tools, such as Ansible and Rundeck.

Integration and Scalability Considerations

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

Real-World Applications and Extensions

• Reviewing case studies focused on performance tuning, downtime prevention, and cost optimization.
• Expanding pipelines by incorporating tracing tools or service graph visualizations.
• Adhering to best practices for operating and maintaining AIOps in production.

Summary and Future Steps