Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open-Source Tools

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

Configuring Prometheus and Grafana

• Installing and setting up Prometheus for time series data collection
• Building dashboards in Grafana using live metrics
• Investigating exporters, relabeling techniques, and service discovery

Preprocessing Data for ML

• Retrieving and modifying Prometheus metrics
• Curating datasets for anomaly detection and forecasting tasks
• Leveraging Grafana transformations or Python-based pipelines

Utilizing ML for Anomaly Detection

• Implementing basic ML models for outlier identification (e.g., Isolation Forest, One-Class SVM)
• Training and assessing models on time series data
• Displaying detected anomalies within Grafana dashboards

Forecasting Metrics via ML

• Developing simple forecasting models (Introduction to ARIMA, Prophet, LSTM)
• Anticipating system load or resource consumption
• Utilizing forecasts for early warnings and scaling strategies

Connecting ML with Alerting and Automation

• Establishing alert rules driven by ML outputs or specific thresholds
• Employing Alertmanager and managing notification routing
• Initiating scripts or automation workflows upon anomaly detection

Scaling and Operationalizing AIOps

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

Recap and Future Directions