Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Tools

• Fundamentals of AIOps: concepts and advantages
• The role of Prometheus and Grafana within the observability ecosystem
• The intersection of ML and AIOps: predictive vs. reactive analytics

Setting Up Prometheus and Grafana

• Installation and configuration of Prometheus for time-series data collection
• Building real-time dashboards in Grafana using live metrics
• Exploring exporters, relabeling, and service discovery mechanisms

Data Preprocessing for ML

• Extraction and transformation of Prometheus metrics
• Preparing datasets optimized for anomaly detection and forecasting tasks
• Leveraging Grafana’s transformation features or Python pipelines

Applying Machine Learning for Anomaly Detection

• Implementing basic ML models for outlier detection (e.g., Isolation Forest, One-Class SVM)
• Training and assessing models on time-series datasets
• Visualizing detected anomalies through Grafana dashboards

Forecasting Metrics with ML

• Developing foundational forecasting models (ARIMA, Prophet, LSTM introduction)
• Predicting system load and resource utilization trends
• Utilizing predictions for proactive alerting and scaling strategies

Integrating ML with Alerting and Automation

• Defining alert rules based on ML outputs or dynamic thresholds
• Configuring Alertmanager and notification routing strategies
• Triggering scripts or automation workflows upon anomaly detection

Scaling and Operationalizing AIOps

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

Summary and Next Steps