Role-Based Programme
RB1495

AI-Powered Cloud Engineering & Cloud Operations

Intelligent Cloud Automation, Reliability & Cost Optimisation

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Duration
32 Hours
Level
Advanced
Delivery
Instructor-Led
Format
Capability Development

Programme Objectives

  • Develop advanced capability to apply AI across cloud engineering, cloud operations, infrastructure provisioning, monitoring, troubleshooting, optimisation, and governance.
  • Use AI to analyse cloud metrics, logs, configurations, resource inventories, incidents, utilisation data, deployment information, and cost patterns.
  • Build repeatable AI-assisted workflows for provisioning support, incident investigation, capacity management, cost optimisation, change control, and operational reporting.
  • Apply AI to identify resource inefficiencies, configuration drift, abnormal cloud behaviour, reliability risks, recurring incidents, and automation opportunities.
  • Design responsible AI-enabled cloud workflows with appropriate controls for security, privileged access, production changes, financial impact, validation, rollback, and human oversight.

Tools covered

Generative AI AssistantsAI Search & ResearchCoding AssistantsCloud Management PlatformsInfrastructure-as-Code ToolsCloud Monitoring & Observability ToolsLog AnalyticsCost & FinOps AnalyticsCloud Security ToolsReporting & Dashboard ToolsWorkflow Automation

Who should attend

  • Cloud Engineers
  • Senior Cloud Engineers
  • Cloud Operations Engineers
  • Cloud Infrastructure Engineers
  • Cloud Administrators
  • Cloud Platform Engineers
  • Cloud Operations Managers
  • Infrastructure Engineers
  • DevOps Engineers
  • Site Reliability Engineers
  • Cloud Support Engineers
  • Cloud Architects
  • Platform Operations Professionals
  • FinOps & Cloud Cost Professionals
  • Cloud Engineering & Operations Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of cloud computing, infrastructure, cloud operations, DevOps, or platform engineering
  • Familiarity with cloud resources, networking, storage, compute, monitoring, security, deployments, and operational metrics
  • Basic proficiency with technical documentation, command-line environments, scripts, and workplace productivity applications
  • No previous AI course attendance required
  • No advanced programming background required

TOC Modules

Concepts
  • Understanding Generative AI, analytical AI, coding assistants, and cloud automation
  • Mapping AI opportunities across provisioning, operations, monitoring, reliability, and optimisation
  • Understanding AI assistance versus authorised cloud engineering decisions
  • Recognising hallucination, privileged-access, security, cost, and production-change risks
Practical activities
  • Mapping an existing cloud engineering and operations workflow
  • Comparing manual and AI-assisted cloud activities
  • Creating a Cloud Operations AI opportunity matrix

Scenarios

Cloud Performance Degradation to Controlled Resolution

Performance Alert → AI-Assisted Monitoring Review → Log & Metric Analysis → Dependency Analysis → Root Cause Investigation → Remediation Plan → Controlled Change → Validation → Monitoring

Participants use AI-assisted techniques to investigate a simulated cloud-performance problem, analyse operational evidence, identify likely causes, develop a controlled remediation plan, and verify service recovery.

Cloud Estate Data to Intelligent Operations & Cost Optimisation

Cloud Inventory + Utilisation + Cost Data + Alerts + Security Findings + Incident History → AI Analysis → Risk & Waste Signals → Priority Actions → Automated Follow-Up → Cloud Dashboard → Operations Review

Participants design an AI-enabled Cloud Engineering & Operations workflow that consolidates technical, cost, and reliability signals, identifies optimisation opportunities and operational risks, automates safe follow-up, and strengthens cloud visibility while retaining privileged and production decisions with authorised cloud professionals.

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