Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Tools

• Overview of AIOps concepts and advantages
• The role of Prometheus and Grafana within the observability stack
• The place of ML in AIOps: predictive versus reactive analytics

Configuring Prometheus and Grafana

• Installing and configuring Prometheus for time series data collection
• Building dashboards in Grafana using live metrics
• Examining exporters, relabelling, and service discovery

Data Preprocessing for Machine Learning

• Extracting and transforming Prometheus metrics
• Preparing datasets for anomaly detection and forecasting tasks
• Leveraging Grafana's transformation features or Python pipelines

Applying Machine Learning for Anomaly Detection

• Foundational ML models for outlier detection (e.g., Isolation Forest, One-Class SVM)
• Training and assessing models on time series datasets
• Displaying detected anomalies in Grafana dashboards

Forecasting Metrics with Machine Learning

• Developing basic forecasting models (ARIMA, Prophet, LSTM introduction)
• Anticipating system load or resource consumption
• Utilising predictions for proactive alerting and scaling strategies

Integrating ML with Alerting and Automation

• Establishing alert rules based on ML outputs or defined thresholds
• Utilising Alertmanager and routing notifications
• Initiating scripts or automation workflows upon anomaly detection

Scaling and Operationalising AIOps

• Incorporating external observability tools (e.g., ELK stack, Moogsoft, Dynatrace)
• Implementing ML models within observability pipelines
• Best practices for large-scale AIOps deployment

Summary and Next Steps