Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Tools

• Examining core AIOps concepts and their operational benefits
• The role of Prometheus and Grafana within the broader observability stack
• Positioning ML in AIOps: contrasting predictive vs. reactive analytics

Setting Up Prometheus and Grafana

• Installing and configuring Prometheus for robust time series data collection
• Developing dynamic dashboards in Grafana powered by real-time metrics
• Delving into exporters, relabeling mechanisms, and service discovery

Data Preprocessing for ML

• Extracting and transforming Prometheus metrics for analytical use
• Curating high-quality datasets tailored for anomaly detection and forecasting tasks
• Leveraging Grafana’s built-in transformations or custom Python pipelines

Applying Machine Learning for Anomaly Detection

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

Forecasting Metrics with ML

• Developing forecasting models (e.g., ARIMA, Prophet, introductory LSTM concepts)
• Predicting future system load patterns and resource consumption
• Utilizing predictions to drive proactive alerting and scaling strategies

Integrating ML with Alerting and Automation

• Defining sophisticated alert rules based on ML outputs or dynamic thresholds
• Managing Alertmanager configurations and efficient notification routing
• Automating script execution or workflow triggers upon anomaly detection

Scaling and Operationalizing AIOps

• Integrating complementary observability tools (e.g., ELK stack, Moogsoft, Dynatrace)
• Embedding ML models seamlessly into operational observability pipelines
• Adhering to best practices for implementing AIOps at enterprise scale

Summary and Next Steps