Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Tools

• Overview of AIOps concepts and key benefits
• The role of Prometheus and Grafana within the observability stack
• Positioning machine learning in AIOps: predictive versus reactive analytics

Setting Up Prometheus and Grafana

• Installing and configuring Prometheus for time series collection
• Building Grafana dashboards leveraging real-time metrics
• Examining exporters, relabelling, and service discovery mechanisms

Data Preprocessing for Machine Learning

• Extracting and transforming metrics from Prometheus
• Preparing datasets suitable for anomaly detection and forecasting tasks
• Utilising Grafana transformations or Python-based 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 data
• Visualising detected anomalies within Grafana dashboards

Forecasting Metrics with Machine Learning

• Developing simple forecasting models (introduction to ARIMA, Prophet, LSTM)
• Predicting system load and resource consumption
• Leveraging predictions for early warning alerts and scaling decisions

Integrating Machine Learning with Alerting and Automation

• Defining alert rules based on ML outputs or static thresholds
• Managing Alertmanager and notification routing
• 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