Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps Using Open Source Solutions

• Key concepts and advantages of AIOps
• The role of Prometheus and Grafana within the observability architecture
• The position of ML in AIOps: contrasting predictive and reactive analysis

Configuring Prometheus and Grafana

• Deployment and setup of Prometheus for time-series data capture
• Designing Grafana dashboards powered by live metrics
• Investigation of exporters, relabeling, and service discovery mechanisms

Data Preparation for Machine Learning

• Extraction and transformation of Prometheus metrics
• Structuring datasets for anomaly detection and forecasting tasks
• Leveraging Grafana’s built-in transformations or Python-based pipelines

Utilizing Machine Learning for Anomaly Detection

• Introduction to foundational ML models for outlier identification (e.g., Isolation Forest, One-Class SVM)
• Model training and assessment on time-series datasets
• Representing anomalies through Grafana dashboard visualizations

Metric Forecasting via Machine Learning

• Construction of basic forecasting models (ARIMA, Prophet, introductory LSTM)
• Projection of system load or resource consumption trends
• Leveraging predictions for proactive alerting and scaling strategies

Connecting ML to Alerting and Automation

• Establishing alert rules derived from ML outputs or predefined thresholds
• Implementation of Alertmanager and notification routing logic
• Initiation of scripts or automation workflows upon anomaly detection

Scaling and Operationalizing AIOps

• Integration with external observability platforms (e.g., ELK stack, Moogsoft, Dynatrace)
• Embedding ML models into observability workflows
• Recommended best practices for large-scale AIOps deployment

Conclusion and Recommended Next Steps