Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Tools

• Key concepts and benefits of AIOps.
• The role of Prometheus and Grafana in the observability stack.
• The place of ML in AIOps: predictive vs. reactive analytics.

Setting Up Prometheus and Grafana

• Installing and configuring Prometheus for time series data collection.
• Building Grafana dashboards utilizing real-time metrics.
• Exploring exporters, relabeling, and service discovery mechanisms.

Data Preprocessing for ML

• Extracting and transforming metrics from Prometheus.
• Preparing datasets specifically for anomaly detection and forecasting tasks.
• Utilizing Grafana’s transformation capabilities or Python-based pipelines.

Applying Machine Learning for Anomaly Detection

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

Forecasting Metrics with ML

• Developing simple forecasting models (Introduction to ARIMA, Prophet, and LSTM).
• Predicting system load or resource utilization patterns.
• Leveraging predictions for early alerting and scaling decisions.

Integrating ML with Alerting and Automation

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

Scaling and Operationalizing AIOps

• Integrating 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