Implementing AIOps with Prometheus, Grafana, and ML Training Course

Foundations of AIOps with Open-Source Solutions

• Exploring the core concepts and strategic advantages of AIOps
• The role of Prometheus and Grafana within the modern observability ecosystem
• The place of ML in AIOps: contrasting predictive versus reactive analytics

Initializing the Prometheus and Grafana Environment

• Deployment and configuration of Prometheus for efficient time-series ingestion
• Designing dynamic dashboards in Grafana utilizing live metrics
• Detailed exploration of exporters, relabeling techniques, and service discovery

Preparing Data for Machine Learning

• Extraction and transformation workflows for Prometheus metrics
• Dataset preparation strategies tailored for anomaly detection and forecasting models
• Leveraging Grafana’s native transformations or Python-based pipelines

Implementing ML for Anomaly Identification

• Application of foundational ML models for outlier detection (e.g., Isolation Forest, One-Class SVM)
• Processes for training and evaluating models against time-series datasets
• Visualization of detected anomalies within Grafana interfaces

Predictive Analytics for System Metrics

• Development of forecasting models using techniques such as ARIMA, Prophet, and introductory LSTM
• Prediction of system loads and resource consumption patterns
• Utilizing predictions to drive early warning systems and scaling decisions

Synergy Between ML, Alerting, and Automation

• Formulating alert rules that respond to ML outputs or dynamic thresholds
• Configuration of Alertmanager and advanced notification routing
• Initiating scripts and automation workflows upon anomaly detection

Scaling and Operationalizing AIOps

• Integration with broader observability platforms (e.g., ELK stack, Moogsoft, Dynatrace)
• Strategies for operationalizing ML models within observability pipelines
• Industry best practices for deploying AIOps at enterprise scale

Conclusion and Future Pathways