Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps using Open Source Tools

• Overview of AIOps core concepts and associated benefits
• The role of Prometheus and Grafana in the observability stack
• The place of ML in AIOps: contrasting predictive and reactive analytics

Configuring Prometheus and Grafana

• Setting up and configuring Prometheus for time series data collection
• Building Grafana dashboards powered by real-time metrics
• Investigating exporters, relabeling mechanisms, and service discovery

Data Preprocessing for Machine Learning

• Extracting and transforming Prometheus metrics
• Structuring datasets for anomaly detection and forecasting tasks
• Leveraging Grafana transformations or Python-based pipelines

Leveraging Machine Learning for Anomaly Detection

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

Forecasting Metrics with ML

• Constructing basic forecasting models (introduction to ARIMA, Prophet, and LSTM)
• Anticipating system load and resource consumption
• Utilizing predictions to inform early alerting and scaling strategies

Integrating ML with Alerting and Automation

• Establishing alert rules based on ML outputs or defined thresholds
• Implementing Alertmanager and managing notification routing
• Initiating scripts or automation workflows upon anomaly detection

Scaling and Operationalizing AIOps

• Connecting external observability solutions (e.g., ELK stack, Moogsoft, Dynatrace)
• Integrating ML models into observability pipelines for production use
• Best practices for managing AIOps at scale

Recap and Future Directions