Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Tools

• Overview of AIOps principles and advantages.
• The role of Prometheus and Grafana within the observability stack.
• The place of ML in AIOps: contrasting predictive and reactive analytics.

Setting Up Prometheus and Grafana

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

Data Preprocessing for ML

• Extraction and transformation of Prometheus metrics.
• Preparing datasets suitable for anomaly detection and forecasting models.
• Utilizing Grafana’s transformation features or Python-based pipelines.

Applying Machine Learning for Anomaly Detection

• Foundational ML models for outlier detection, such as Isolation Forest and One-Class SVM.
• Training and assessing models using time series data.
• Visualizing detected anomalies within Grafana dashboards.

Forecasting Metrics with ML

• Developing simple forecasting models, including ARIMA, Prophet, and introductory LSTM concepts.
• Anticipating system load or resource utilization trends.
• Leveraging predictions for early alerting and scaling decisions.

Integrating ML with Alerting and Automation

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

Scaling and Operationalizing AIOps

• Integration with external observability platforms like the ELK stack, Moogsoft, or Dynatrace.
• Operationalizing ML models within observability pipelines.
• Best practices for implementing AIOps at scale.

Summary and Next Steps