Implementing AIOps with Prometheus, Grafana, and ML Training Course

Foundations of AIOps with Open Source Solutions

• Key AIOps concepts and their operational benefits
• The role of Prometheus and Grafana in the observability ecosystem
• Positioning ML in AIOps: balancing predictive and reactive analytics

Establishing the Prometheus and Grafana Environment

• Installation and configuration of Prometheus for time series data collection
• Developing dynamic Grafana dashboards using live metrics
• Utilizing exporters, relabeling, and service discovery mechanisms

Preparing Data for Machine Learning

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

Implementing ML for Anomaly Detection

• Applying foundational ML models for outlier identification (e.g., Isolation Forest, One-Class SVM)
• Training and validating models against time series data
• Visualizing detected anomalies within Grafana dashboards

Forecasting Metrics Through ML

• Developing forecasting models using ARIMA, Prophet, or introductory LSTM approaches
• Predicting system load patterns and resource utilization
• Utilizing forecasts to drive early alerting and scaling strategies

Connecting ML with Alerting and Automation

• Crafting alert rules based on ML outputs or dynamic thresholds
• Configuring Alertmanager and optimizing notification routing
• Initiating scripts or automation workflows upon anomaly detection

Scaling and Operationalizing AIOps

• Integrating with external observability platforms (e.g., ELK stack, Moogsoft, Dynatrace)
• Embedding ML models into continuous observability pipelines
• Adopting best practices for AIOps at scale

Course Conclusion and Future Directions