Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Tools

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

Setting Up Prometheus and Grafana

• Installation and configuration of Prometheus for time series data collection
• Building Grafana dashboards powered by real-time metrics
• Exploring exporters, relabeling mechanisms, and service discovery

Data Preprocessing for Machine Learning

• Extracting and transforming metrics from Prometheus
• Curating datasets for anomaly detection and forecasting models
• Leveraging Grafana transformations or Python-based data pipelines

Applying Machine Learning for Anomaly Detection

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

Forecasting Metrics with Machine Learning

• Developing introductory forecasting models (ARIMA, Prophet, LSTM basics)
• Predicting system load and resource utilization trends
• Utilizing predictions to enable early alerting and proactive scaling

Integrating ML with Alerting and Automation

• Defining alert rules based on ML outputs or dynamic thresholds
• Managing notifications through Alertmanager and routing strategies
• Triggering automation scripts or 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 implementing AIOps at enterprise scale

Summary and Next Steps