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Course Outline
Designing an Open AIOps Architecture
- Overview of essential 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.
- Developing Kibana dashboards for log analytics.
- Leveraging Elasticsearch queries to extract operational insights.
Anomaly Detection and Incident Prediction
- Exporting observability data to Python pipelines.
- Training machine learning models for outlier detection and forecasting.
- Deploying models for live inference within the observability pipeline.
Alerting and Automation with Open Tools
- Configuring Prometheus alert rules and Alertmanager routing.
- Triggering scripts or API workflows for automated response.
- Utilizing open-source orchestration tools (e.g., Ansible, Rundeck).
Integration and Scalability Considerations
- Managing high-volume ingestion and long-term retention.
- Security and access control within 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
- Familiarity with observability tools such as Prometheus or ELK.
- Proficiency in Python and foundational knowledge of machine learning.
- Understanding of IT operations and alerting workflows.
Target Audience
- Senior Site Reliability Engineers (SREs).
- Data engineers working within operations teams.
- DevOps platform leads and infrastructure architects.
14 Hours
