Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps Using Open Source Tools

• Understanding AIOps concepts and their advantages
• The role of Prometheus and Grafana in the observability ecosystem
• The place of ML in AIOps: comparing predictive and reactive analytics

Configuring Prometheus and Grafana

• Installing and tuning Prometheus for time series data collection
• Building Grafana dashboards with real-time metrics
• Working with exporters, relabeling, and service discovery

Preparing Data for Machine Learning

• Extracting and processing Prometheus metrics
• Preparing datasets for anomaly detection and forecasting tasks
• Leveraging Grafana transformations or Python-based pipelines

Leveraging Machine Learning for Anomaly Detection

• Introduction to ML models for outlier detection (e.g., Isolation Forest, One-Class SVM)
• Training and assessing models on time series data
• Displaying anomalies within Grafana dashboards

Predicting Metrics with Machine Learning

• Developing basic forecasting models (intro to ARIMA, Prophet, LSTM)
• Anticipating system load and resource consumption
• Utilizing predictions for proactive alerting and scaling decisions

Combining ML with Alerting and Automation

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

Scaling and Operationalizing AIOps

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

Recap and Future Directions