Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open Source Solutions

• Key concepts and advantages of AIOps.
• The role of Prometheus and Grafana within the observability ecosystem.
• The position of machine learning in AIOps: comparing predictive and reactive analytics.

Configuring Prometheus and Grafana

• Installation and setup of Prometheus for time series data collection.
• Designing Grafana dashboards driven by real-time metrics.
• Investigating exporters, relabeling processes, and service discovery.

Preparing Data for Machine Learning

• Extraction and transformation of Prometheus metrics.
• Structuring datasets suitable for anomaly detection and forecasting tasks.
• Utilizing Grafana’s transformation features or Python-based data pipelines.

Utilizing Machine Learning for Anomaly Detection

• Core machine learning models for outlier identification (e.g., Isolation Forest, One-Class SVM).
• Model training and evaluation using time series data.
• Visualizing detected anomalies within Grafana dashboards.

Metric Forecasting via Machine Learning

• Development of basic forecasting models (including ARIMA, Prophet, and LSTM introductions).
• Anticipating system load or resource consumption patterns.
• Leveraging predictions for proactive alerting and scaling decisions.

Machine Learning Integration with Alerting and Automation

• Creating alert rules driven by machine learning outputs or specific thresholds.
• Implementing Alertmanager and managing notification routing.
• Initiating scripts or automation workflows upon anomaly detection.

Scaling and Operationalizing AIOps

• Incorporating external observability platforms (e.g., ELK stack, Moogsoft, Dynatrace).
• Managing machine learning models within observability pipelines.
• Best practices for implementing AIOps at scale.

Conclusion and Recommendations for Next Steps