Role-Based Programme
RB1492

AI-Powered Cloud Engineering & Cloud Operations

Smarter Provisioning, Monitoring & Cloud Reliability

IMAGE REQUIRED
Duration
4 Hours
Level
Awareness
Delivery
Instructor-Led
Format
Awareness Session

Programme Objectives

  • Understand how AI can support Cloud Engineering and Cloud Operations across provisioning, monitoring, troubleshooting, optimisation, and documentation.
  • Explore practical prompting techniques for cloud configurations, incidents, logs, infrastructure changes, and operational reporting.
  • Apply AI to organise technical information, support root-cause investigation, document cloud environments, and improve recurring operational tasks.
  • Identify opportunities to improve cloud visibility, engineering productivity, reliability, and operational efficiency.
  • Recognise security, access control, cost, production safety, architecture validation, and human-review responsibilities when using AI.

Tools covered

Generative AI AssistantsAI-Assisted Cloud TroubleshootingInfrastructure-as-Code SupportCloud MonitoringLog AnalysisCost & Capacity ReviewCloud DocumentationBasic Workflow Automation

Who should attend

  • Cloud Engineers
  • Cloud Operations Engineers
  • Cloud Administrators
  • Cloud Infrastructure Engineers
  • Platform Engineers
  • DevOps Engineers
  • Site Reliability Engineers
  • Cloud Support Engineers
  • Cloud Architects
  • Infrastructure Engineers
  • Cloud Operations Analysts
  • Cloud Platform Administrators
  • Cloud Engineering Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of cloud computing, infrastructure, or IT operations
  • Familiarity with cloud services, monitoring, deployments, or infrastructure concepts is helpful
  • Basic computer and technical skills
  • No advanced AI or machine-learning knowledge required
  • No previous Generative AI experience required

TOC Modules

Concepts
  • Understanding Generative AI and its relevance to cloud engineering and operational environments
  • Identifying AI applications across provisioning, monitoring, troubleshooting, optimisation, and documentation
  • Understanding AI assistance versus Cloud Engineer and Operations professional judgement
  • Recognising production, architecture, and security-sensitive activities requiring human validation
Practical activities
  • Mapping common Cloud Engineering and Operations activities to potential AI applications
  • Comparing a traditional cloud-operations task with an AI-assisted approach

Scenarios

Cloud Requirement to Deployment Readiness Checklist

Cloud Requirement → AI-Assisted Resource Mapping → Configuration → Dependencies → Security & Capacity Checks → Validation → Readiness Summary

Participants use AI to organise a sample cloud requirement into a structured deployment-readiness checklist covering resources, dependencies, configuration considerations, validation steps, and operational risks.

Cloud Alert to Operational Resolution Summary

Cloud Alert → Logs & Metrics → AI-Assisted Triage → Possible Causes → Validation Steps → Recovery Actions → Operational Summary

Participants use AI to analyse sample cloud alerts and logs, structure troubleshooting steps, and prepare a concise operational resolution summary while retaining all production changes and recovery decisions with authorised cloud professionals.

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