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Course Outline
Introduction to AIOps for Government
- Origins and Evolution of AIOps for Government
- Role of AIOps in Modern IT Operations for Government
- Comparison with Traditional IT Operations Analytics for Government
Organizational Context for AIOps in the Public Sector
- Drivers and Strategic Impact of AIOps for Government
- Integration with DevOps and SRE Practices for Government
- Security and Complexity Considerations for Government IT Operations
Core Technologies - Data Fundamentals for AIOps in Government
- Big Data Concepts and the 5 Vs for Government
- Data Sources, Diversity, and Processing Challenges for Government IT
Core Technologies - Machine Learning (ML) for AIOps in Government
- Roles of AI and ML in AIOps for Government
- Supervised vs. Unsupervised Learning for Government Applications
- ML Models Utilized in AIOps for Government IT Operations
Operational Metrics in AIOps for Government
- Key Metrics: SLA, SLO, KPI for Government IT
- Incident Metrics: MTTD, MTTR, MTBF, MTTA for Government Operations
Use Cases and Mindset Shift for AIOps in Government
- Reactive vs. Proactive Operations for Government IT
- Real-World Examples of AIOps Implementation for Government
- Organizational Change Impacts for Government Agencies
Implementation Strategies for AIOps in Government
- Common Pitfalls and Success Factors for Government IT
- Data Quality and Alignment for Government Operations
- Ethics, Compliance, and Data Protection for Government IT
Requirements
- A foundational knowledge of IT operations and system monitoring principles
- Practical experience in IT environments and data telemetry
Audience
- IT operations teams and managers for government agencies
- DevOps/SRE practitioners
- Cloud and infrastructure professionals
- Data engineers and analysts
14 Hours