Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Solutions

• Key AIOps concepts and their strategic benefits
• The role of Prometheus and Grafana within the observability stack
• Positioning ML in AIOps: comparing predictive versus reactive analytics

Configuring Prometheus and Grafana

• Installing and tuning Prometheus for time series data collection
• Developing Grafana dashboards using real-time metrics
• Investigating exporters, relabeling strategies, and service discovery

Preparing Data for Machine Learning

• Extracting and transforming Prometheus metrics
• Crafting datasets suitable for anomaly detection and forecasting
• Leveraging Grafana transformations or Python data pipelines

Utilizing Machine Learning for Anomaly Detection

• Foundational 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

Predicting Metrics via Machine Learning

• Developing basic forecasting models (ARIMA, Prophet, introduction to LSTM)
• Anticipating system load and resource consumption
• Utilizing predictions for proactive alerting and scaling decisions

Integrating ML into Alerting and Automation

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

Scaling and Operationalizing AIOps

• Incorporating external observability tools (e.g., ELK stack, Moogsoft, Dynatrace)
• Embedding ML models into observability pipelines
• Best practices for implementing AIOps at scale

Conclusion and Path Forward