AIOps Fundamentals Training in Madrid, Spain

This course provides foundational knowledge in AIOps, focusing on monitoring, event correlation, and intelligent alerting. Participants will explore how AI and machine learning enhance IT operations by reducing noise, identifying patterns, and proactively addressing incidents. The course includes both conceptual understanding and practical implementation techniques.

Foundations of AIOps and Intelligent Monitoring

Introduction to AIOps

• Definition and scope of AIOps
• AIOps in the context of ITOM, DevOps, and SRE
• Key components: monitoring, analytics, automation, and collaboration
• Benefits: faster incident response, cost reduction, improved uptime

Monitoring Evolution and AIOps Integration

• Traditional monitoring vs AI-enhanced monitoring
• Metrics, logs, traces, and topology as data sources
• Role of data volume, velocity, and variety in AIOps
• Real-world examples of intelligent monitoring in use

Anomaly Detection and Pattern Recognition

• Types of anomalies: point, contextual, collective
• Statistical vs machine learning approaches
• Use of baselines and dynamic thresholds
• Benefits of real-time anomaly detection

Event Correlation and Intelligent Alerting

Event Correlation in AIOps

• Importance of correlation in noisy environments
• Event storms, duplicate alerts, and false positives
• Topology-aware and time-series correlation
• Rule-based vs AI-driven correlation engines

Designing Intelligent Alerting Systems

• Characteristics of intelligent alerts: relevance, accuracy, context
• Alert enrichment with metadata, logs, and incident history
• Suppression, deduplication, and escalation logic
• Integrating with ticketing, incident response, and chatops tools

Tooling Landscape and Architecture Design

• Overview of AIOps platforms
• Building blocks of an AIOps pipeline
• Data ingestion, model training, feedback loops
• Choosing the right architecture for your organization

Final Use Case Walkthrough and Assessment

• Design a basic AIOps monitoring and alerting architecture
• Walk through a correlated alert scenario from detection to resolution
• Identify improvement areas using intelligent feedback
• Review questions and recap of key takeaways