Building an AIOps Pipeline with Open Source Tools Training Course

Designing an Open AIOps Architecture

• Key components within open AIOps pipelines
• Data pathways from ingestion through to alerting
• Tool evaluation and integration approaches

Data Collection and Aggregation

• Importing time-series data via Prometheus
• Logging capture using Logstash and Beats
• Standardizing data for cross-source correlation

Creating Observability Dashboards

• Metric visualization with Grafana
• Kibana dashboard development for log analysis
• Leveraging Elasticsearch queries to derive operational insights

Anomaly Detection and Incident Forecasting

• Transferring observability data to Python pipelines
• Training ML models for outlier identification and prediction
• Deploying models for live inference within the observability workflow

Alerting and Automation via Open Tools

• Defining Prometheus alert rules and Alertmanager routing
• Initiating scripts or API workflows for automatic response
• Utilizing open-source orchestration solutions (e.g., Ansible, Rundeck)

Integration and Scalability Factors

• Managing high-volume ingestion and long-term data retention
• Security and access management within open-source stacks
• Independent scaling of layers: ingestion, processing, and alerting

Practical Applications and Extensions

• Case studies: performance optimization, outage prevention, and cost reduction
• Expanding pipelines with tracing tools or service graph analysis
• Best practices for operating and maintaining AIOps in production

Conclusion and Future Actions