Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps Using Open Source Tools

• Key concepts and advantages of AIOps
• The role of Prometheus and Grafana within the observability stack
• The position of ML in AIOps: predictive versus reactive analytics

Configuring Prometheus and Grafana

• Deployment and setup of Prometheus for time series data collection
• Building Grafana dashboards with real-time metrics
• Examining exporters, relabeling strategies, and service discovery

Data Preprocessing for Machine Learning

• Extracting and refining Prometheus metrics
• Constructing datasets suitable for anomaly detection and forecasting
• Leveraging Grafana transformations or Python-based pipelines

Leveraging Machine Learning for Anomaly Detection

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

Forecasting Metrics via Machine Learning

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

Integrating ML into Alerting and Automation

• Establishing alert rules driven by ML outputs or defined thresholds
• Implementing Alertmanager and notification routing
• Activating scripts or automation workflows upon anomaly detection

Scaling and Operationalizing AIOps

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

Conclusion and Recommended Next Steps