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
• The place of ML in AIOps: predictive versus reactive analytics

Setting Up Prometheus and Grafana

• Installation and configuration of Prometheus for time series data collection
• Building dashboards in Grafana using live metrics
• Exploring exporters, relabeling techniques, and service discovery mechanisms

Data Preprocessing for Machine Learning

• Extraction and transformation of metrics from Prometheus
• Dataset preparation for anomaly detection and forecasting tasks
• Leveraging Grafana transformations or Python pipelines for data processing

Applying Machine Learning for Anomaly Detection

• Fundamental ML models for outlier identification (e.g., Isolation Forest, One-Class SVM)
• Model training and evaluation using time series data
• Visualization of detected anomalies within Grafana dashboards

Forecasting Metrics with Machine Learning

• Construction of basic forecasting models (ARIMA, Prophet, introductory LSTM)
• Prediction of system load and resource utilization patterns
• Utilizing predictions to enable early alerting and scaling decisions

Integrating ML with Alerting and Automation

• Creation of alert rules based on ML outputs or predefined thresholds
• Configuration of Alertmanager and notification routing strategies
• Automation of scripts and workflows triggered by anomaly detection

Scaling and Operationalizing AIOps

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

Summary and Future Steps