Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Tools

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

Setting Up Prometheus and Grafana

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

Data Preprocessing for ML

• Extracting and transforming Prometheus metrics
• Preparing datasets for anomaly detection and forecasting
• Utilizing Grafana’s transformations or Python pipelines

Applying Machine Learning for Anomaly Detection

• Basic ML models for outlier detection (e.g., Isolation Forest, One-Class SVM)
• Training and evaluating models on time series data
• Visualizing anomalies within Grafana dashboards

Forecasting Metrics with ML

• Building simple forecasting models (ARIMA, Prophet, LSTM introduction)
• Predicting system load and resource usage
• Leveraging predictions for early alerting and scaling decisions

Integrating ML with Alerting and Automation

• Defining alert rules based on ML outputs or thresholds
• Utilizing Alertmanager and notification routing
• Triggering scripts or automation workflows upon anomaly detection

Scaling and Operationalizing AIOps

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

Summary and Next Steps