Building an AIOps Pipeline with Open Source Tools Training Course

Architecting an Open AIOps Framework

• Introduction to essential components in open AIOps pipelines
• Data progression from ingestion to alert generation
• Comparative analysis of tools and integration strategies

Data Acquisition and Aggregation

• Importing time-series data via Prometheus
• Capturing log data using Logstash and Beats
• Standardizing data for cross-source analysis

Developing Observability Dashboards

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

Anomaly Identification and Incident Forecasting

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

Alerting and Automation via Open-Source Tools

• Defining 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 stacks
• Scaling individual layers independently: ingestion, processing, and alerting

Practical Applications and Extensions

• Case studies: performance optimization, uptime assurance, and cost reduction
• Enhancing pipelines with tracing utilities or service mapping
• Best practices for operating and maintaining AIOps in production

Overview and Subsequent Steps