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Course Outline

Overview of Artificial Intelligence for IT Operations (AIOps)

  • Historical development and progression of AIOps
  • AIOps functions within contemporary IT environments
  • Distinctions between AIOps and conventional IT operations analytics

Strategic Context for AIOps Adoption

  • Fundamental drivers and strategic implications of AIOps
  • Synergies with DevOps and Site Reliability Engineering (SRE) frameworks
  • Security protocols and management of systemic complexity

Foundational Technologies - Data Infrastructure

  • Big Data principles and the five defining characteristics
  • Data acquisition sources, heterogeneity, and processing challenges

Foundational Technologies - Machine Learning (ML)

  • The function of AI and ML within AIOps architectures
  • Differentiating supervised from unsupervised learning methodologies
  • Selecting appropriate ML models for AIOps applications

Performance Indicators in AIOps

  • Primary operational metrics: Service Level Agreement (SLA), Service Level Objective (SLO), Key Performance Indicator (KPI)
  • Incident management metrics: Mean Time to Detect (MTTD), Mean Time to Resolve (MTTR), Mean Time Between Failures (MTBF), and Mean Time to Acknowledge (MTTA)

Application Scenarios and Cultural Transition

  • Shifting from reactive troubleshooting to proactive management
  • Analytical case studies for government agencies
  • Impact on organizational structures and workflows

Deployment Approaches

  • Anticipated challenges and critical success factors
  • Ensuring data integrity and strategic alignment
  • Ethical standards, regulatory compliance, and information protection for government systems

Requirements

  • Demonstrated proficiency in fundamental IT operational procedures and system surveillance frameworks
  • Practical background in managing IT infrastructures and processing data telemetry

Audience

  • IT operations personnel and leadership
  • DevOps and Site Reliability Engineering staff
  • Cloud computing and infrastructure specialists
  • Data engineering and analytical professionals
 14 Hours

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