Building an AIOps Pipeline with Open Source Tools Training Course

Designing an Open AIOps Architecture

• A review of the primary elements in open AIOps pipelines
• Tracing data movement from initial ingestion to final alerting
• Comparing tools and defining integration strategies

Data Collection and Aggregation

• Importing time-series data via Prometheus
• Log collection using Logstash and Beats
• Standardizing data to enable correlation across multiple sources

Creating Observability Dashboards

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

Anomaly Detection and Incident Forecasting

• Channeling observability data into Python pipelines
• Training ML models to identify outliers and predict trends
• Deploying models for real-time inference within the observability workflow

Alerting and Automation via Open Tools

• Establishing Prometheus alert rules and configuring Alertmanager routing
• Initiating scripts or API workflows for automated response
• Employing open-source orchestration platforms such as Ansible and Rundeck

Integration and Scalability Factors

• Managing high-volume data ingestion and extended retention periods
• Implementing security and access controls within open-source stacks
• Independently scaling ingestion, processing, and alerting layers

Practical Applications and Extensions

• Case studies covering performance tuning, downtime avoidance, and cost efficiency
• Expanding pipelines with tracing utilities or service maps
• Best practices for operating and sustaining AIOps in production environments

Recap and Future Directions