Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps Using Open Source Tools

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

Configuring Prometheus and Grafana

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

Data Preprocessing for Machine Learning

• Extraction and transformation of Prometheus metrics.
• Preparing datasets optimized for anomaly detection and forecasting.
• Utilizing Grafana transformations or Python-based pipelines.

Machine Learning for Anomaly Detection

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

Metric Forecasting with Machine Learning

• Developing forecasting models, including ARIMA, Prophet, and an introduction to LSTM.
• Predicting system load and resource utilization.
• Leveraging predictions for early warning systems and scaling decisions.

Integrating ML into Alerting and Automation

• Formulating alert rules based on ML outputs or defined thresholds.
• Managing notifications using Alertmanager and routing configurations.
• Executing scripts or automation workflows triggered by 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.

Recap and Future Directions