Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Tools

• Understanding AIOps fundamentals and their organizational benefits
• The role of Prometheus and Grafana within the observability ecosystem
• Positioning ML in AIOps: distinguishing between predictive and reactive analytics

Configuration of Prometheus and Grafana

• Installing and tuning Prometheus for efficient time series data collection
• Designing effective dashboards in Grafana using live metrics
• Investigating exporters, label relabeling, and service discovery mechanisms

Data Preparation for Machine Learning

• Extracting and processing Prometheus metrics for analysis
• Structuring datasets suitable for anomaly detection and forecasting tasks
• Utilizing Grafana’s transformation features or Python-based pipelines for data processing

Machine Learning Applications in Anomaly Detection

• Implementing foundational ML models for outlier identification (such as Isolation Forest and One-Class SVM)
• Training and assessing model performance on time series datasets
• Visualizing detected anomalies directly within Grafana dashboards

Metric Forecasting with Machine Learning

• Developing introductory forecasting models (including ARIMA, Prophet, and LSTM)
• Anticipating system load and resource consumption patterns
• Leveraging predictions to inform early warning alerts and scaling strategies

Integrating ML into Alerting and Automation

• Creating alert rules based on ML outputs or dynamic thresholds
• Configuring Alertmanager and setting up notification routing
• Automating workflows or scripts in response to detected anomalies

Scaling and Operationalizing AIOps

• Connecting with external observability platforms (such as the ELK stack, Moogsoft, or Dynatrace)
• Deploying ML models into continuous observability pipelines
• Adopting best practices for managing AIOps at scale

Conclusion and Future Directions