Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
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