Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps Using Open Source Tools

• Overview of AIOps principles and their advantages
• The role of Prometheus and Grafana within the observability ecosystem
• Positioning Machine Learning in AIOps: predictive versus reactive analysis

Configuring Prometheus and Grafana

• Deployment and setup of Prometheus for time series data acquisition
• Building dashboards in Grafana leveraging live metrics
• Investigation of exporters, relabeling rules, and service discovery mechanisms

Data Preparation for Machine Learning

• Retrieval and conversion of Prometheus metrics
• Structuring datasets for anomaly detection and prediction tasks
• Utilizing Grafana transformation features or Python-based data pipelines

Implementing Machine Learning for Anomaly Detection

• Foundational ML algorithms for outlier identification (e.g., Isolation Forest, One-Class SVM)
• Model training and validation using time series datasets
• Displaying detected anomalies through Grafana visualizations

Metric Forecasting via Machine Learning

• Construction of basic prediction models (ARIMA, Prophet, introduction to LSTM)
• Anticipating system load and resource consumption
• Leveraging forecasts for proactive alerting and scaling strategies

Connecting Machine Learning with Alerting and Automation

• Establishing alert criteria driven by ML outputs or static thresholds
• Configuration of Alertmanager and notification workflows
• Initiating scripts or automated workflows upon anomaly identification

Scaling and Implementing AIOps Operations

• Integration with external observability platforms (e.g., ELK stack, Moogsoft, Dynatrace)
• Deployment of ML models within observability data pipelines
• Best practices for managing AIOps at scale

Conclusion and Future Actions