Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open-Source Tools

• Overview of AIOps concepts and advantages.
• The role of Prometheus and Grafana within the observability stack.
• The place of ML in AIOps: comparative analysis of predictive and reactive insights.

Configuring Prometheus and Grafana

• Setting up and configuring Prometheus for time-series data collection.
• Building dashboards in Grafana using live metrics.
• Investigating exporters, relabeling, and service discovery mechanisms.

Data Preprocessing for Machine Learning

• Extracting and transforming metrics from Prometheus.
• Preparing datasets suitable for anomaly detection and forecasting tasks.
• Leveraging Grafana transformations or Python-based pipelines.

Applying Machine Learning for Anomaly Detection

• Foundational ML models for outlier detection (such as Isolation Forest and One-Class SVM).
• Training and assessing models on time-series data.
• Displaying anomalies within Grafana dashboards.

Forecasting Metrics with Machine Learning

• Developing basic forecasting models (including ARIMA, Prophet, and an introduction to LSTM).
• Predicting system load or resource consumption patterns.
• Utilizing predictions for early warning and scaling decisions.

Integrating Machine Learning with Alerting and Automation

• Creating 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 (such as the ELK stack, Moogsoft, and Dynatrace).
• Operationalizing ML models within observability pipelines.
• Best practices for implementing AIOps at scale.

Summary and Future Steps