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Course Outline
Introduction to AIOps
- Origins and evolution of AIOps
- Role of AIOps in modern IT operations
- Comparison with traditional IT operations analytics
Organizational Context for AIOps
- AIOps drivers and strategic impact
- Integration with DevOps and SRE
- Security and complexity considerations
Core Technologies - Data Fundamentals
- Big Data concepts and the 5 Vs
- Data sources, diversity, and processing challenges
Core Technologies - Machine Learning (ML)
- AI and ML roles in AIOps
- Supervised vs unsupervised learning
- ML models used in AIOps
Operational Metrics in AIOps
- Key metrics: SLA, SLO, KPI
- Incident metrics: MTTD, MTTR, MTBF, MTTA
Use Cases and Mindset Shift
- Reactive vs proactive operations
- Real-world examples
- Organizational change impacts
Implementation Strategies
- Common pitfalls and success factors
- Data quality and alignment
- Ethics, compliance, and data protection
Requirements
- An understanding of basic IT operations and system monitoring concepts
- Experience with IT environments and data telemetry
Audience
- IT operations teams and managers
- DevOps/SRE practitioners
- Cloud and infrastructure professionals
- Data engineers and analysts
14 Hours
