Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Tools

• An overview of AIOps concepts and their organisational benefits
• The role of Prometheus and Grafana within the observability stack
• Positioning machine learning in AIOps: contrasting predictive and reactive analytics

Setting Up Prometheus and Grafana

• Installing and configuring Prometheus for effective time series data collection
• Building dashboards in Grafana using real-time metrics
• Exploring exporters, relabeling, and service discovery mechanisms

Data Preprocessing for Machine Learning

• Extracting and transforming metrics from Prometheus
• Preparing datasets suitable for anomaly detection and forecasting models
• Leveraging Grafana’s native transformations or Python-based pipelines

Applying Machine Learning for Anomaly Detection

• Implementing basic ML models for outlier detection (e.g., Isolation Forest, One-Class SVM)
• Training and evaluating models using time series data
• Visualising detected anomalies within Grafana dashboards

Forecasting Metrics with Machine Learning

• Developing simple forecasting models (introductory ARIMA, Prophet, and LSTM)
• Predicting system load and resource utilisation patterns
• Utilising predictions for early alerting and scaling decisions

Integrating Machine Learning with Alerting and Automation

• Defining alert rules based on machine learning outputs or defined thresholds
• Configuring Alertmanager and routing notifications effectively
• Triggering scripts or automation workflows upon anomaly detection

Scaling and Operationalising AIOps

• Integrating external observability tools (e.g., ELK stack, Moogsoft, Dynatrace)
• Operationalising ML models within observability pipelines
• Best practices for implementing AIOps at scale

Summary and Next Steps