Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Tools

• Core AIOps concepts and their strategic benefits
• The role of Prometheus and Grafana within the observability stack
• Positioning ML in AIOps: contrasting predictive and reactive analytics

Setting Up Prometheus and Grafana

• Installation and configuration of Prometheus for time series data collection
• Building real-time metrics dashboards in Grafana
• Deep dive into exporters, relabeling, and service discovery mechanisms

Data Preprocessing for ML

• Extraction and transformation of Prometheus metrics
• Curating datasets optimized for anomaly detection and forecasting tasks
• Utilizing Grafana’s transformation features or Python-based pipelines

Applying Machine Learning for Anomaly Detection

• Introduction to foundational ML models for outlier detection (e.g., Isolation Forest, One-Class SVM)
• Training and evaluating models using time series datasets
• Visualizing detected anomalies directly on Grafana dashboards

Forecasting Metrics with ML

• Developing introductory forecasting models (ARIMA, Prophet, LSTM)
• Predicting trends in system load and resource consumption
• Leveraging predictions to facilitate early alerting and scaling decisions

Integrating ML with Alerting and Automation

• Crafting alert rules that respond to ML outputs or predefined thresholds
• Managing notifications and routing via Alertmanager
• Automating script execution and workflows in response to detected anomalies

Scaling and Operationalizing AIOps

• Integrating complementary observability tools (e.g., ELK stack, Moogsoft, Dynatrace)
• Embedding ML models into broader observability pipelines for operational use
• Best practices for managing AIOps at enterprise scale

Summary and Next Steps