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Course Outline
Designing an Open AIOps Architecture
- Overview of essential components within open AIOps pipelines.
- Data flow progression from ingestion to alerting.
- Tool comparison and integration strategies.
Data Collection and Aggregation
- Ingesting time-series data using Prometheus.
- Capturing logs with Logstash and Beats.
- Normalizing data for effective cross-source correlation.
Building Observability Dashboards
- Visualizing metrics through Grafana.
- Developing Kibana dashboards for log analytics.
- Leveraging 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 real-time 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.
- Ensuring security and access control within open-source stacks.
- Independently scaling each layer: ingestion, processing, and alerting.
Real-World Applications and Extensions
- Case studies focusing on performance tuning, downtime prevention, and cost optimization.
- Extending pipelines with tracing tools or service graphs.
- Best practices for operating and maintaining AIOps in production environments.
Summary and Next Steps
Requirements
- Prior experience with observability tools such as Prometheus or ELK.
- Functional proficiency in Python and foundational machine learning concepts.
- Solid understanding of IT operations and alerting workflows.
Target Audience
- Advanced Site Reliability Engineers (SREs).
- Data engineers specializing in operational contexts.
- DevOps platform leads and infrastructure architects.
14 Hours
