Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Tools

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

Setting Up Prometheus and Grafana

• Installation and configuration of Prometheus for efficient time series collection
• Designing dashboards in Grafana utilizing real-time metrics
• Exploring exporters, relabeling strategies, and service discovery mechanisms

Data Preprocessing for Machine Learning

• Extracting and transforming metrics from Prometheus
• Preparing datasets suitable for anomaly detection and forecasting models
• Utilizing Grafana’s native transformations or Python-based data pipelines

Applying Machine Learning for Anomaly Detection

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

Forecasting Metrics with Machine Learning

• Constructing basic forecasting models (Introduction to ARIMA, Prophet, and LSTM)
• Predicting system load or resource utilization trends
• Leveraging predictions for early alerting and capacity scaling decisions

Integrating ML with Alerting and Automation

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

Scaling and Operationalizing AIOps

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

Summary and Next Steps