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Course Outline
Designing an Open AIOps Architecture
- Overview of core components in open AIOps pipelines.
- Data flow from ingestion to alerting.
- Tool comparison and integration strategies.
Data Collection and Aggregation
- Ingesting time-series data with Prometheus.
- Capturing logs using Logstash and Beats.
- Standardizing data for cross-source correlation.
Building Observability Dashboards
- Visualizing metrics with Grafana.
- Creating Kibana dashboards for log analytics.
- Using Elasticsearch queries to derive 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 within the observability pipeline.
Alerting and Automation with Open Tools
- Creating Prometheus alert rules and configuring Alertmanager routing.
- Triggering scripts or API workflows for automated responses.
- Utilizing open-source orchestration tools (e.g., Ansible, Rundeck).
Integration and Scalability Considerations
- Managing high-volume ingestion and long-term data retention.
- Security and access control in open-source stacks.
- Scaling each layer independently: ingestion, processing, and 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
Requirements
- Experience with observability tools like Prometheus or ELK.
- Proficiency in Python and fundamental machine learning concepts.
- Understanding of IT operations and alerting workflows.
Target Audience
- Senior Site Reliability Engineers (SREs).
- Data engineers involved in operations.
- DevOps platform leads and infrastructure architects.
14 Hours
