Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps Using Open Source Tools

• Key concepts and strategic benefits of AIOps
• The role of Prometheus and Grafana in the modern observability stack
• The place of ML in AIOps: contrasting predictive and reactive analytics

Setting Up Prometheus and Grafana

• Installation and configuration of Prometheus for time series data collection
• Building dynamic dashboards in Grafana utilizing real-time metrics
• Investigating exporters, relabeling rules, and service discovery mechanisms

Data Preprocessing for Machine Learning

• Techniques for extracting and transforming Prometheus metrics
• Preparing robust datasets for anomaly detection and forecasting tasks
• Implementing data transformations via Grafana features or Python pipelines

Applying Machine Learning for Anomaly Detection

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

Forecasting Metrics with Machine Learning

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

Integrating Machine Learning with Alerting and Automation

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

Scaling and Operationalizing AIOps

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

Summary and Next Steps