Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Tools

• Exploring core AIOps concepts and their strategic benefits
• The role of Prometheus and Grafana within the observability stack
• Positioning ML in AIOps: shifting from reactive to predictive analytics

Setting Up Prometheus and Grafana

• Installing and configuring Prometheus for efficient time series data collection
• Building interactive dashboards in Grafana utilizing real-time metrics
• Managing exporters, relabeling configurations, and service discovery mechanisms

Data Preprocessing for ML

• Extracting and transforming raw Prometheus metrics for analysis
• Structuring datasets to support anomaly detection and forecasting models
• Utilizing 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 validating models using time series datasets
• Visualizing detected anomalies directly within Grafana dashboards

Forecasting Metrics with ML

• Developing forecasting models using ARIMA, Prophet, and introductory LSTM techniques
• Predicting trends in system load and resource consumption
• Leveraging predictions to enable early warning alerts and proactive scaling decisions

Integrating ML with Alerting and Automation

• Defining alerting rules based on ML outputs or dynamic thresholds
• Configuring Alertmanager and optimizing notification routing strategies
• Automating workflows and scripts triggered by anomaly detection events

Scaling and Operationalizing AIOps

• Integrating external observability platforms (e.g., ELK stack, Moogsoft, Dynatrace)
• Operationalizing ML models within large-scale observability pipelines
• Adopting best practices for deploying AIOps at scale

Summary and Next Steps