Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps using Open-Source Tools

• Overview of AIOps concepts and their advantages
• The role of ML in AIOps: predictive versus reactive analytics

Configuring Prometheus and Grafana

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

Data Preprocessing for Machine Learning

• Extraction and transformation of Prometheus metrics
• Preparation of datasets suited for anomaly detection and forecasting
• Utilisation of Grafana’s transformation features or Python pipelines

Applying Machine Learning for Anomaly Detection

• Fundamental ML models for outlier detection (e.g., Isolation Forest, One-Class SVM)
• Training and model evaluation on time-series data
• Visualising detected anomalies within Grafana dashboards

Forecasting Metrics via Machine Learning

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

Integrating ML with Alerting and Automation

• Definition of alert rules based on ML outputs or defined thresholds
• Implementation of Alertmanager and notification routing
• Initiation of scripts or automation workflows upon anomaly detection

Scaling and Operationalising AIOps

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

Summary and Future Directions