Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Solutions

• Key concepts and advantages of AIOps
• The role of Prometheus and Grafana within the observability architecture
• Positioning ML in AIOps: distinguishing between predictive and reactive analytics

Configuring Prometheus and Grafana

• Deployment and configuration of Prometheus for time series data acquisition
• Designing Grafana dashboards utilizing live metrics
• In-depth exploration of exporters, relabeling mechanisms, and service discovery

Data Preparation for Machine Learning

• Extraction and transformation of Prometheus metrics
• Curating datasets suitable for anomaly detection and forecasting tasks
• Utilizing Grafana’s built-in transformations or Python-based data pipelines

Machine Learning for Anomaly Detection

• Implementation of basic ML models for outlier identification (e.g., Isolation Forest, One-Class SVM)
• Model training and evaluation processes on time series data
• Visualization of detected anomalies within Grafana interfaces

Metrics Forecasting via Machine Learning

• Construction of foundational forecasting models (including ARIMA, Prophet, and an introduction to LSTM)
• Prediction of system load and resource consumption patterns
• Leveraging forecasts for proactive alerting and scaling strategies

Integrating ML with Alerting and Automation

• Formulation of alert rules based on ML outputs or defined thresholds
• Configuration of Alertmanager and notification routing paths
• Execution of scripts or automation workflows in response to detected anomalies

Scaling and Operationalizing AIOps

• Integration with external observability platforms (e.g., ELK stack, Moogsoft, Dynatrace)
• Operational deployment of ML models within observability pipelines
• Best practices for managing AIOps at scale

Summary and Future Steps