Building an AIOps Pipeline with Open Source Tools Training Course

Architecting an Open-Source AIOps Framework

• Key elements within open AIOps pipelines.
• The journey of data from ingestion to alerting.
• Comparing tools and defining integration strategies.

Data Acquisition and Consolidation

• Ingesting time-series data via Prometheus.
• Capturing logs using Logstash and Beats.
• Standardizing data for cross-source correlation.

Creating Observability Dashboards

• Visualizing metrics with Grafana.
• Constructing Kibana dashboards for log analysis.
• Deriving operational insights through Elasticsearch queries.

Anomaly Identification and Incident Forecasting

• Exporting observability data to Python workflows.
• Training ML models for outlier detection and prediction.
• Deploying models for live inference within the observability stream.

Alerting and Automation via Open-Source Tools

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

Integration and Scalability Strategies

• Managing high-volume ingestion and long-term data retention.
• Implementing security and access controls in open-source stacks.
• Scaling layers independently: ingestion, processing, and alerting.

Practical Applications and Expansions

• Case studies focusing on performance tuning, downtime avoidance, and cost reduction.
• Enhancing pipelines with tracing tools or service maps.
• Best practices for operating and maintaining AIOps in production.

Recap and Future Actions