Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Solutions

• Key concepts and strategic advantages of AIOps.
• The role of Prometheus and Grafana within the observability ecosystem.
• Positioning ML in AIOps: contrasting predictive and reactive analytics.

Initializing Prometheus and Grafana

• Installation and configuration of Prometheus for time series data collection.
• Developing Grafana dashboards utilizing real-time metrics.
• Investigating exporters, relabeling mechanisms, and service discovery.

Preparing Data for Machine Learning

• Extraction and transformation of Prometheus metrics.
• Dataset preparation for anomaly detection and forecasting tasks.
• Utilizing Grafana’s transformation features or Python-based data pipelines.

Machine Learning for Anomaly Detection

• Foundational ML models for outlier identification (e.g., Isolation Forest, One-Class SVM).
• Model training and evaluation using time series data.
• Visualization of detected anomalies within Grafana dashboards.

Metric Forecasting with Machine Learning

• Construction of forecasting models (ARIMA, Prophet, and introductory LSTM concepts).
• Predicting system loads and resource consumption patterns.
• Leveraging predictions for proactive alerting and scaling decisions.

Integrating ML with Alerting and Automation

• Formulating alert rules based on ML outputs or defined thresholds.
• Configuring Alertmanager and notification routing strategies.
• Automating workflows or script execution in response to detected anomalies.

Scaling and Operationalizing AIOps

• Integration with external observability platforms (e.g., ELK stack, Moogsoft, Dynatrace).
• Deploying ML models within observability pipelines.
• Best practices for implementing AIOps at scale.

Conclusion and Future Directions