Course Outline

Designing an Open AIOps Architecture

  • Overview of key 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
  • Building Kibana dashboards for log analytics
  • Using Elasticsearch queries to extract 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 in the observability pipeline

Alerting and Automation with Open Tools

  • Creating Prometheus alert rules and Alertmanager routing
  • Triggering scripts or API workflows for auto-response
  • Using open-source orchestration tools (e.g., Ansible, Rundeck)

Integration and Scalability Considerations

  • Handling high-volume ingestion and long-term retention
  • Security and access control in open-source stacks
  • Scaling each layer independently: ingestion, processing, 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 such as Prometheus or ELK
  • Working knowledge of Python and machine learning fundamentals
  • Understanding of IT operations and alerting workflows

Audience

  • Advanced site reliability engineers (SREs)
  • Data engineers working in operations
  • DevOps platform leads and infrastructure architects
 14 Hours

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