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Course Outline
Designing an Open AIOps Architecture
- Overview of essential components in open AIOps pipelines
- Data flow from ingestion through to alerting
- Tool comparison and integration strategy
Data Collection and Aggregation
- Ingesting time-series data using Prometheus
- Capturing logs with Logstash and Beats
- Standardizing data for cross-source correlation
Building Observability Dashboards
- Visualizing metrics with Grafana
- Developing Kibana dashboards for log analytics
- Utilizing 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 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
- Leveraging 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
- Scaling individual layers independently: 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 running and maintaining AIOps in production environments
Summary and Next Steps
Requirements
- Familiarity with observability tools like Prometheus or ELK
- Practical knowledge of Python and foundational machine learning concepts
- Understanding of IT operations and alerting workflows
Target Audience
- Senior site reliability engineers (SREs)
- Data engineers focusing on operational roles
- DevOps platform leads and infrastructure architects
14 Hours
