Advanced AIOps Training in Kraków, Poland

Advanced AIOps Training is designed for IT professionals seeking to leverage artificial intelligence and machine learning to transform IT operations. This course covers the core principles of AIOps, including data ingestion, event correlation, anomaly detection, and automated remediation. Participants will explore real-world use cases, best practices for implementing AIOps platforms, and strategies for reducing noise and improving mean time to resolution (MTTR). The training emphasizes practical skills for deploying AIOps in complex, multi-cloud environments.

Introduction to AIOps

• Defining AIOps and its evolution
• Key drivers for AIOps adoption
• Relationship between AIOps and DevOps
• Common challenges in modern IT operations

Hands-on exercises

Data Ingestion and Normalization

• Sources of operational data
• Data collection strategies
• Normalization and enrichment techniques
• Handling streaming and batch data

Hands-on exercises

Machine Learning Fundamentals for AIOps

• Supervised vs unsupervised learning
• Time series analysis and forecasting
• Anomaly detection algorithms
• Model training and evaluation

Hands-on exercises

Event Correlation and Root Cause Analysis

• Correlation techniques and algorithms
• Topology-based correlation
• Statistical and ML-driven RCA
• Reducing alert fatigue

Hands-on exercises

Automated Incident Response

• Incident lifecycle automation
• Runbook automation and orchestration
• Self-healing infrastructure concepts
• Integration with ITSM and ChatOps

Hands-on exercises

Anomaly Detection and Predictive Analytics

• Static vs dynamic thresholds
• Seasonal and trend decomposition
• Predictive maintenance use cases
• Real-time anomaly detection pipelines

Hands-on exercises

Implementing AIOps in Multi-Cloud Environments

• Monitoring across AWS, Azure, and GCP
• Unified observability strategies
• Data privacy and compliance considerations
• Cost optimization with AIOps

Hands-on exercises

AIOps Platform Evaluation and Integration

• Key features of AIOps platforms
• Open source vs commercial solutions
• Integration with existing monitoring tools
• Pilot deployment and scaling

Hands-on exercises

Advanced Topics and Future Trends

• AIOps and MLOps synergy
• Explainable AI in operations
• Edge AIOps and IoT
• Preparing for next-generation AIOps

Hands-on exercises