Implementing AIOps with Prometheus, Grafana, and ML

• An understanding of system monitoring and observability concepts
• Experience using Grafana or Prometheus
• Familiarity with Python and basic machine learning principles

Audience

• Observability engineers
• Infrastructure and DevOps teams
• Monitoring platform architects and site reliability engineers (SREs)

Prometheus and Grafana are widely adopted tools for observability in modern infrastructure, while machine learning enhances these tools with predictive and intelligent insights to automate operations decisions.

This instructor-led, live training (online or onsite) is aimed at intermediate-level observability professionals who wish to modernize their monitoring infrastructure by integrating AIOps practices using Prometheus, Grafana, and ML techniques.

By the end of this training, participants will be able to:

• Configure Prometheus and Grafana for observability across systems and services.
• Collect, store, and visualize high-quality time series data.
• Apply machine learning models for anomaly detection and forecasting.
• Build intelligent alerting rules based on predictive insights.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to AIOps with Open Source Tools

• Overview of AIOps concepts and benefits
• Prometheus and Grafana in the observability stack
• Where ML fits in AIOps: predictive vs. reactive analytics

Setting Up Prometheus and Grafana

• Installing and configuring Prometheus for time series collection
• Creating dashboards in Grafana using real-time metrics
• Exploring exporters, relabeling, and service discovery

Data Preprocessing for ML

• Extracting and transforming Prometheus metrics
• Preparing datasets for anomaly detection and forecasting
• Using Grafana’s transformations or Python pipelines

Applying Machine Learning for Anomaly Detection

• Basic ML models for outlier detection (e.g., Isolation Forest, One-Class SVM)
• Training and evaluating models on time series data
• Visualizing anomalies in Grafana dashboards

Forecasting Metrics with ML

• Building simple forecasting models (ARIMA, Prophet, LSTM intro)
• Predicting system load or resource usage
• Using predictions for early alerting and scaling decisions

Integrating ML with Alerting and Automation

• Defining alert rules based on ML output or thresholds
• Using Alertmanager and notification routing
• Triggering scripts or automation workflows on anomaly detection

Scaling and Operationalizing AIOps

• Integrating external observability tools (e.g., ELK stack, Moogsoft, Dynatrace)
• Operationalizing ML models in observability pipelines
• Best practices for AIOps at scale

Summary and Next Steps