AIOps with Prometheus, Grafana and ML Training in Madrid, Spain

This hands-on course teaches participants how to build intelligent IT operations using Prometheus and Grafana, enhanced with machine learning capabilities. The course covers advanced monitoring, anomaly detection, alerting, and visualization, along with ML integration to enable predictive and automated operations workflows.

Advanced Monitoring with Prometheus and Grafana

Introduction to Intelligent Monitoring

• Defining AIOps in the context of Prometheus and Grafana
• Overview of time-series monitoring
• Benefits of ML in observability pipelines
• Architecture for AIOps with Prometheus + Grafana + ML

Prometheus Deep Dive

• Data scraping, exporters, and relabeling
• Writing effective PromQL queries
• Best practices for metric naming and retention
• Federation and scaling in large environments

Grafana for Operational Insights

• Advanced dashboard features and templating
• Using transformations, variables, and drilldowns
• Real-time vs historical data views
• Sharing, exporting, and alerting from Grafana panels

Alerting and Anomaly Detection with Machine Learning

Prometheus Alerting Concepts

• Alert rules vs recording rules
• Alertmanager setup and routing
• Silence, inhibition, and grouping alerts
• Alert reliability and anti-flapping techniques

Anomaly Detection Techniques

• Static vs dynamic thresholds
• Time-series forecasting and outlier detection
• Use cases for contextual and seasonal anomalies
• Approaches: Holt-Winters, ARIMA, Z-score, Isolation Forest

ML Integration in Grafana and Prometheus

• Grafana ML plugins (Grafana Machine Learning, Prophet panel, etc.)
• Using Grafana with Python-based ML (Jupyter, Flask API)
• External anomaly detection pipelines and webhooks to Alertmanager
• Integrating with external ML tools (e.g., TensorFlow, Scikit-learn)

Automating Intelligence and Building AIOps Pipelines

Predictive Monitoring and Trend Analysis

• Long-term trend analysis with Prometheus and Grafana
• Forecasting system load, memory, or latency
• Early warning systems with prediction intervals
• Custom ML models for specific operational metrics

Alert Enrichment and Automation

• Enhancing alerts with logs, tags, and historical data
• Integration with incident management tools
• Triggering remediation workflows via webhooks or automation tools
• Alert correlation and deduplication patterns

Building a Complete AIOps Stack

• Full pipeline: exporters → Prometheus → ML → Grafana → Alerting
• Review open source ML tool options: Sktime, Prophet, PyCaret, etc.
• Key architectural considerations for enterprise use
• Security, scalability, and maintainability

Final Assessment and Project Review

• Wrap-up, Q&A, and next steps for real-world adoption