Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Tools

• Key concepts and advantages of AIOps
• The role of Prometheus and Grafana within the observability stack
• Integrating ML into AIOps: predictive versus reactive analytics

Configuring Prometheus and Grafana

• Installation and configuration of Prometheus for time series data collection
• Building Grafana dashboards utilizing real-time metrics
• Exploring exporters, relabeling mechanisms, and service discovery

Data Preprocessing for Machine Learning

• Extraction and transformation of Prometheus metrics
• Dataset preparation for anomaly detection and forecasting tasks
• Utilizing Grafana transformations or Python-based pipelines

Machine Learning for Anomaly Detection

• Foundational ML models for outlier detection (e.g., Isolation Forest, One-Class SVM)
• Training and evaluating models using time series data
• Visualizing detected anomalies within Grafana dashboards

Metric Forecasting with Machine Learning

• Developing forecasting models (ARIMA, Prophet, LSTM introduction)
• Predicting system load and resource consumption patterns
• Leveraging predictions for early alerting and scaling decisions

Integrating ML with Alerting and Automation

• Formulating alert rules based on ML outputs or predefined thresholds
• Implementing Alertmanager and notification routing strategies
• Initiating scripts or automation workflows upon anomaly detection

Scaling and Operationalizing AIOps

• Integration with external observability tools (e.g., ELK stack, Moogsoft, Dynatrace)
• Operationalizing ML models within observability pipelines
• Best practices for implementing AIOps at scale

Summary and Future Directions