Implementing AIOps with Prometheus, Grafana, and ML Training Course

Fundamentals of AIOps with Open Source Tools

• Core AIOps concepts and their organizational benefits
• The role of Prometheus and Grafana in the observability ecosystem
• Positioning ML within AIOps: predictive vs. reactive analytics

Deployment of Prometheus and Grafana

• Installing and configuring Prometheus for time-series data collection
• Building dashboards in Grafana utilizing live metrics
• Investigating exporters, relabeling, and service discovery mechanisms

Data Preparation for Machine Learning

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

Leveraging Machine Learning for Anomaly Detection

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

Metric Forecasting Using ML

• Developing simple forecasting models (intro to ARIMA, Prophet, LSTM)
• Anticipating system load and resource consumption
• Applying predictions for proactive alerting and scaling strategies

Integrating ML with Alerting and Automation

• Establishing alert rules based on ML outputs or defined thresholds
• Utilizing Alertmanager and notification routing
• Activating scripts or automation workflows upon anomaly detection

Scaling and Implementing AIOps Operations

• Connecting external observability tools (e.g., ELK stack, Moogsoft, Dynatrace)
• Operationalizing ML models within observability pipelines
• Best practices for AIOps at scale

Recap and Future Directions