Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps Using Open Source Solutions

• Core AIOps concepts and their advantages
• The role of Prometheus and Grafana in the observability stack
• The place of ML in AIOps: predictive versus reactive analysis

Configuring Prometheus and Grafana

• Installing and setting up Prometheus for time series data collection
• Building Grafana dashboards using live metrics
• Managing exporters, relabeling, and service discovery

Preparing Data for Machine Learning

• Extracting and processing Prometheus metrics
• Curating datasets suitable for anomaly detection and forecasting
• Applying Grafana transformations or Python-based data pipelines

Utilizing 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 forecasting models (ARIMA, Prophet, and an introduction to LSTM)
• Anticipating system load and resource consumption
• Leveraging predictions for proactive alerting and scaling strategies

Combining ML with Alerting and Automation

• Establishing alert rules driven by ML insights or static thresholds
• Implementing Alertmanager and notification routing
• Initiating scripts or automation workflows upon anomaly detection

Scaling and Implementing AIOps in Production

• Connecting external observability tools (e.g., ELK stack, Moogsoft, Dynatrace)
• Operationalizing ML models within observability workflows
• Best practices for scaling AIOps implementations

Recap and Future Pathways