Building an AIOps Pipeline with Open Source Tools Training Course

Designing an Open-Source AIOps Architecture

• Exploration of essential components within open AIOps pipelines
• Tracing the data journey from ingestion through to alerting
• Comparing tools and defining integration strategies

Data Acquisition and Aggregation

• Capturing time-series data via Prometheus
• Gathering logs using Logstash and Beats
• Standardizing data structures to enable cross-source correlation

Developing Observability Dashboards

• Rendering metrics visualizations with Grafana
• Constructing Kibana interfaces for log analysis
• Leveraging Elasticsearch queries to uncover operational insights

Detecting Anomalies and Forecasting Incidents

• Transferring observability data into Python-based pipelines
• Training ML models for identifying outliers and forecasting trends
• Deploying models for real-time inference within the observability stack

Implementing Alerting and Automation with Open Tools

• Defining Prometheus alert rules and configuring Alertmanager routing
• Executing scripts or API workflows to trigger automated responses
• Utilizing open-source orchestration platforms (such as Ansible and Rundeck)

Addressing Integration and Scalability Needs

• Managing high-volume data ingestion and long-term storage retention
• Enforcing security and access controls within open-source stacks
• Scaling individual layers independently, including ingestion, processing, and alerting

Practical Applications and Future Extensions

• Analyzing case studies on performance optimization, downtime mitigation, and cost reduction
• Augmenting pipelines with tracing utilities or service graph visualizations
• Adopting best practices for the operation and maintenance of AIOps in production

Recap and Subsequent Steps