Building an AIOps Pipeline with Open Source Tools Training Course

Designing an Open AIOps Architecture

• Introduction to essential components within open AIOps pipelines
• Mapping the data journey from ingestion to alerting
• Evaluating tools and defining integration strategies

Data Collection and Aggregation

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

Creating Observability Dashboards

• Presenting metrics visually with Grafana
• Developing Kibana dashboards for detailed log analysis
• Leveraging Elasticsearch queries to uncover operational insights

Anomaly Detection and Incident Prediction

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

Alerting and Automation via Open Tools

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

Integration and Scalability Factors

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

Real-World Applications and Advanced Extensions

• Case studies focusing on performance optimization, downtime avoidance, and cost efficiency
• Enhancing pipelines through tracing tools or service graph implementations
• Best practices for sustaining and operating AIOps systems in production

Recap and Future Directions