Building an AIOps Pipeline with Open Source Tools Training Course

Designing an Open AIOps Architecture

• An overview of the essential components within open AIOps pipelines
• Mapping the data journey from ingestion to alerting
• Evaluating tool comparisons and integration strategies

Data Collection and Aggregation

• Acquiring time-series data via Prometheus
• Log capture using Logstash and Beats
• Standardising data to enable cross-source correlation

Building Observability Dashboards

• Metric visualisation with Grafana
• Creating Kibana dashboards for detailed log analytics
• Leveraging Elasticsearch queries to derive operational insights

Anomaly Detection and Incident Prediction

• Feeding observability data into Python pipelines
• Developing ML models for outlier detection and forecasting
• Deploying models for real-time inference within the observability pipeline

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 ingestion and long-term data retention
• Implementing security and access control within open-source stacks
• Independent scaling of each layer: ingestion, processing, and alerting

Real-World Applications and Extensions

• Case studies covering performance tuning, downtime prevention, and cost optimisation
• Augmenting pipelines with tracing tools or service graphs
• Best practices for operating and sustaining AIOps in production

Summary and Next Steps