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
• Positioning ML in AIOps: contrasting predictive and reactive analytics

Setting Up Prometheus and Grafana

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

Data Preprocessing for ML

• Extracting and transforming Prometheus metrics
• Preparing datasets suitable for anomaly detection and forecasting
• Leveraging Grafana’s transformations or Python pipelines for data preparation

Applying Machine Learning for Anomaly Detection

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

Forecasting Metrics with ML

• Developing simple forecasting models (ARIMA, Prophet, LSTM intro)
• Predicting system load or resource usage patterns
• Utilizing predictions for early alerting and scaling decisions

Integrating ML with Alerting and Automation

• Defining alert rules based on ML output or predefined thresholds
• Configuring 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 scaling AIOps implementations

Summary and Next Steps