Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Tools

• Explaining AIOps fundamentals and their advantages
• The role of Prometheus and Grafana in the observability architecture
• The position of ML in AIOps: comparing predictive and reactive analytics

Configuring Prometheus and Grafana

• Deployment and configuration of Prometheus for time series data acquisition
• Designing Grafana dashboards powered by real-time metrics
• Investigating exporters, relabeling mechanisms, and service discovery

Data Preprocessing for ML

• Retrieving and processing Prometheus metrics
• Structuring datasets for anomaly detection and predictive modeling
• Utilizing Grafana transformations or Python-based pipelines

Leveraging Machine Learning for Anomaly Detection

• Implementing foundational ML models for outlier identification (e.g., Isolation Forest, One-Class SVM)
• Training and assessing models using time series data
• Displaying detected anomalies within Grafana dashboards

Metrics Forecasting with ML

• Developing basic forecasting models (ARIMA, Prophet, introductory LSTM)
• Anticipating system load and resource consumption patterns
• Utilizing predictions to trigger early alerts and scaling adjustments

Integrating ML into Alerting and Automation

• Creating alert rules derived from ML outputs or defined thresholds
• Managing notifications and routing via Alertmanager
• Initiating scripts or automated workflows upon anomaly detection

Scaling and Operationalizing AIOps

• Incorporating third-party observability solutions (e.g., ELK stack, Moogsoft, Dynatrace)
• Embedding ML models into observability workflows
• Best practices for implementing AIOps at a large scale

Conclusion and Future Directions