Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Tools

• Exploration of AIOps core concepts and their operational benefits
• The role of Prometheus and Grafana within the observability stack
• Positioning ML in AIOps: distinguishing between predictive and reactive analytics

Setting Up Prometheus and Grafana

• Installation and configuration of Prometheus for effective time series collection
• Designing Grafana dashboards that utilize real-time metrics
• In-depth review of exporters, relabeling, and service discovery mechanisms

Data Preprocessing for ML

• Techniques for extracting and transforming Prometheus metrics
• Preparing datasets optimized for anomaly detection and forecasting tasks
• Utilizing Grafana’s native transformations or Python-based pipelines

Applying Machine Learning for Anomaly Detection

• Introduction to basic ML models for outlier detection, such as Isolation Forest and One-Class SVM
• Processes for training and evaluating models against time series data
• Strategies for visualizing detected anomalies within Grafana dashboards

Forecasting Metrics with ML

• Development of simple forecasting models, including an introduction to ARIMA, Prophet, and LSTM
• Methods for predicting system load and resource usage patterns
• Leveraging predictions to drive early alerting and scaling decisions

Integrating ML with Alerting and Automation

• Formulating alert rules based on ML outputs or dynamic thresholds
• Implementing Alertmanager and configuring notification routing
• Automating workflows and script triggers in response to detected anomalies

Scaling and Operationalizing AIOps

• Integration with external observability tools, such as the ELK stack, Moogsoft, and Dynatrace
• Strategies for operationalizing ML models within observability pipelines
• Best practices for implementing AIOps at scale

Summary and Next Steps