Building an AIOps Pipeline with Open Source Tools Training Course

Designing an Open AIOps Architecture

• Review of essential components within open AIOps pipelines.
• Mapping data flow from initial ingestion through to alerting.
• Evaluating tools and defining integration strategies.

Data Acquisition and Consolidation

• Processing time-series data via Prometheus.
• Log capture using Logstash and Beats.
• Standardizing data to enable cross-source correlation.

Creating Observability Dashboards

• Metric visualization using Grafana.
• Developing Kibana dashboards for log analysis.
• Leveraging Elasticsearch queries to derive operational insights.

Anomaly Detection and Incident Forecasting

• Exporting observability data into Python pipelines.
• Training ML models for outlier identification and prediction.
• Implementing models for real-time inference within the observability stack.

Alerting and Automation via Open-Source Tools

• Defining Prometheus alert rules and configuring 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 data retention.
• Implementing security and access controls in open-source stacks.
• Independently scaling layers: ingestion, processing, and alerting.

Practical Applications and Advanced Extensions

• Case studies covering performance tuning, downtime avoidance, and cost efficiency.
• Enhancing pipelines with tracing utilities or service mapping.
• Best practices for operationalizing and maintaining AIOps in production.

Conclusion and Future Directions