Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Tools

• Exploring core AIOps concepts and their strategic benefits.
• The role of Prometheus and Grafana within the observability stack.
• Positioning ML in AIOps: contrasting predictive and reactive analytics.

Configuring Prometheus and Grafana

• Installing and setting up Prometheus for efficient time-series data collection.
• Building dynamic dashboards in Grafana utilizing real-time metrics.
• Investigating exporters, relabeling mechanisms, and service discovery.

Preparing Data for Machine Learning

• Extracting and transforming metrics from Prometheus.
• Structuring datasets specifically for anomaly detection and forecasting tasks.
• Implementing data preparation using Grafana transformations or Python pipelines.

Implementing Machine Learning for Anomaly Detection

• Foundations of ML models for outlier identification (e.g., Isolation Forest, One-Class SVM).
• Processes for training and evaluating models against time-series data.
• Visualizing detected anomalies directly within Grafana dashboards.

Generating Forecasts with ML

• Developing introductory forecasting models (including ARIMA, Prophet, and LSTM).
• Anticipating system loads and resource utilization trends.
• Utilizing predictions to trigger early alerts and inform scaling decisions.

Integrating ML with Alerting and Automation

• Crafting alert rules based on ML outputs or dynamic thresholds.
• Leveraging Alertmanager for advanced notification routing.
• Initiating scripts or automation workflows upon anomaly detection.

Scaling and Operationalizing AIOps

• Connecting external observability solutions (such as the ELK stack, Moogsoft, or Dynatrace).
• Deploying ML models effectively within observability pipelines.
• Adopting best practices for AIOps at an enterprise scale.

Summary and Future Steps