Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Tools

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

Setting Up Prometheus and Grafana

• Installation and configuration of Prometheus for time series data collection.
• Building Grafana dashboards leveraging real-time metrics.
• Deep dive into exporters, relabeling, and service discovery mechanisms.

Data Preprocessing for ML

• Techniques for extracting and transforming Prometheus metrics.
• Preparing datasets suitable for anomaly detection and forecasting tasks.
• Utilizing Grafana’s transformation capabilities or Python-based data pipelines.

Applying Machine Learning for Anomaly Detection

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

Forecasting Metrics with ML

• Developing simple forecasting models (introduction to ARIMA, Prophet, and LSTM).
• Predicting system load and resource consumption trends.
• Leveraging predictions to enable early alerting and informed scaling decisions.

Integrating ML with Alerting and Automation

• Defining alert rules based on ML outputs or dynamic thresholds.
• Managing Alertmanager and notification routing strategies.
• Automating scripts and workflows triggered by 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 scaling AIOps initiatives.

Summary and Next Steps