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Course Outline
Architecting an Open AIOps Framework
- Examination of core components within open AIOps pipelines
- Data movement from ingestion to alert generation
- Tool evaluation and integration strategy
Data Collection and Aggregation
- Ingesting time-series data via Prometheus
- Capturing logs using Logstash and Beats
- Standardising data for cross-source correlation
Developing Observability Dashboards
- Visualising metrics with Grafana
- Constructing Kibana dashboards for log analytics
- Leveraging Elasticsearch queries to derive operational insights
Anomaly Detection and Incident Forecasting
- Transferring observability data into Python pipelines
- Training machine learning models for outlier detection and forecasting
- Deploying models for real-time inference within the observability pipeline
Alerting and Automation Using Open Tools
- Establishing Prometheus alert rules and Alertmanager routing
- Initiating scripts or API workflows for automated responses
- Utilising open-source orchestration tools (e.g., Ansible, Rundeck)
Integration and Scalability Considerations
- Managing high-volume data 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 optimisation
- Extending pipelines with tracing tools or service graphs
- Best practices for operating and maintaining AIOps in production
Summary and Next Steps
Requirements
- Proficiency with observability platforms such as Prometheus or ELK
- Practical knowledge of Python and foundational machine learning concepts
- Comprehension of IT operations and alerting workflows
Target Audience
- Senior site reliability engineers (SREs)
- Data engineers operating within infrastructure teams
- DevOps platform managers and infrastructure architects
14 Hours
