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

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