Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Tools

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

Setting Up Prometheus and Grafana

• Installation and configuration of Prometheus for time series data collection
• Building dashboards in Grafana using real-time metrics
• Exploring exporters, relabeling strategies, and service discovery

Data Preprocessing for ML

• Extracting and transforming metrics from Prometheus
• Preparing datasets optimized for anomaly detection and forecasting
• Utilizing Grafana 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 on time series data
• Visualizing detected anomalies within Grafana dashboards

Forecasting Metrics with ML

• Developing simple forecasting models (ARIMA, Prophet, LSTM introduction)
• Predicting system load and resource utilization trends
• Leveraging predictions for proactive alerting and scaling decisions

Integrating ML with Alerting and Automation

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

Scaling and Operationalizing AIOps

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

Summary and Next Steps