Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Tools

• Core AIOps concepts and their operational benefits
• The role of Prometheus and Grafana within the observability stack
• The place of ML in AIOps: comparing predictive and reactive analytics

Setting Up Prometheus and Grafana

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

Data Preprocessing for Machine Learning

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

Applying Machine Learning for Anomaly Detection

• Fundamentals of ML models for outlier detection (e.g., Isolation Forest, One-Class SVM)
• Training and assessing models using time series data
• Visualizing detected anomalies within Grafana dashboards

Forecasting Metrics with Machine Learning

• Developing simple forecasting models (introduction to ARIMA, Prophet, and LSTM)
• Predicting system load and resource consumption
• Utilizing predictions to drive early alerts and scaling decisions

Integrating Machine Learning with Alerting and Automation

• Establishing alert rules based on ML outputs or defined thresholds
• Implementing Alertmanager and notification routing strategies
• Initiating scripts or automation workflows upon anomaly detection

Scaling and Operationalizing AIOps

• Connecting external observability tools (e.g., ELK stack, Moogsoft, Dynatrace)
• Operationalizing ML models within observability pipelines
• Key best practices for managing AIOps at scale

Summary and Next Steps