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Duration 14 hours
Course Outline
Introduction to AIOps Utilizing Open Source Technologies
- Fundamentals of AIOps and its operational advantages
- The role of Prometheus and Grafana in the observability architecture
- Machine learning integration in AIOps: comparative analysis of predictive and reactive analytics
Implementation of Prometheus and Grafana
- Installation and configuration of Prometheus for time series data acquisition
- Development of Grafana dashboards utilizing real-time metric data
- Examination of exporters, relabeling mechanisms, and service discovery protocols
Data Preprocessing for Machine Learning
- Extraction and transformation of Prometheus metric data
- Dataset preparation for anomaly detection and forecasting algorithms
- Utilization of Grafana transformation features or Python-based processing pipelines
Application of Machine Learning for Anomaly Detection
- Deployment of fundamental ML models for outlier identification (e.g., Isolation Forest, One-Class SVM)
- Model training and performance evaluation using time series datasets
- Visualization of detected anomalies within Grafana dashboard environments
Metrics Forecasting via Machine Learning
- Development of basic forecasting models (ARIMA, Prophet, LSTM introductory concepts)
- Prediction of system load and resource utilization trends
- Application of predictive insights for early warning systems and scaling strategies
Integration of ML with Alerting and Automation
- Definition of alert rules based on ML outputs or static thresholds
- Configuration of Alertmanager and notification routing protocols
- Execution of scripts or automation workflows upon anomaly detection
Scaling and Operationalization of AIOps
- Integration with external observability tools (e.g., ELK stack, Moogsoft, Dynatrace)
- Operationalization of ML models within observability pipelines
- Establishment of best practices for AIOps implementation at scale
Conclusion and Recommended Next Steps
Requirements
- Foundational understanding of system monitoring and observability concepts
- Practical experience utilizing Grafana or Prometheus
- Familiarity with Python and fundamental machine learning principles
Target Audience
- Observability engineers
- Infrastructure and DevOps teams
- Monitoring platform architects and site reliability engineers (SREs)
