Building an AIOps Pipeline with Open Source Tools Training Course

Designing an Open-Source AIOps Architecture

• Introduction to the core components of open AIOps pipelines
• Data pathways from ingestion through to alerting
• Comparing tools and defining integration strategies

Data Acquisition and Aggregation

• Processing time-series data via Prometheus
• Capturing logs using Logstash and Beats
• Standardizing data for effective cross-source correlation

Developing Observability Dashboards

• Visualizing metrics through Grafana
• Creating Kibana dashboards for log analysis
• Leveraging Elasticsearch queries to derive operational insights

Anomaly Detection and Incident Forecasting

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

Alerting and Automation with Open-Source Tools

• Configuring Prometheus alert rules and Alertmanager routing
• Initiating scripts or API workflows for automated responses
• Employing open-source orchestration platforms (e.g., Ansible, Rundeck)

Integration and Scalability Factors

• Managing high-volume data ingestion and long-term storage
• Implementing security and access controls in open-source environments
• Scaling individual layers independently: ingestion, processing, and alerting

Practical Applications and Extensions

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

Wrap-up and Future Directions