Building an AIOps Pipeline with Open Source Tools

• Experience with observability tools such as Prometheus or ELK
• Working knowledge of Python and machine learning fundamentals
• Understanding of IT operations and alerting workflows

Audience

• Advanced site reliability engineers (SREs)
• Data engineers working in operations
• DevOps platform leads and infrastructure architects

An AIOps pipeline built entirely with open-source tools allows teams to design cost-effective and flexible solutions for observability, anomaly detection, and intelligent alerting in production environments.

This instructor-led, live training (online or onsite) is aimed at advanced-level engineers who wish to build and deploy an end-to-end AIOps pipeline using tools like Prometheus, ELK, Grafana, and custom ML models.

By the end of this training, participants will be able to:

• Design an AIOps architecture using only open-source components.
• Collect and normalize data from logs, metrics, and traces.
• Apply ML models to detect anomalies and predict incidents.
• Automate alerting and remediation using open tooling.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Designing an Open AIOps Architecture

• Overview of key components in open AIOps pipelines
• Data flow from ingestion to alerting
• Tool comparison and integration strategy

Data Collection and Aggregation

• Ingesting time-series data with Prometheus
• Capturing logs with Logstash and Beats
• Normalizing data for cross-source correlation

Building Observability Dashboards

• Visualizing metrics with Grafana
• Building Kibana dashboards for log analytics
• Using Elasticsearch queries to extract operational insights

Anomaly Detection and Incident Prediction

• Exporting observability data to Python pipelines
• Training ML models for outlier detection and forecasting
• Deploying models for live inference in the observability pipeline

Alerting and Automation with Open Tools

• Creating Prometheus alert rules and Alertmanager routing
• Triggering scripts or API workflows for auto-response
• Using open-source orchestration tools (e.g., Ansible, Rundeck)

Integration and Scalability Considerations

• Handling high-volume ingestion and long-term retention
• Security and access control in open-source stacks
• Scaling each layer independently: ingestion, processing, alerting

Real-World Applications and Extensions

• Case studies: performance tuning, downtime prevention, and cost optimization
• Extending pipelines with tracing tools or service graphs
• Best practices for running and maintaining AIOps in production

Summary and Next Steps