Role-Based Programme
RB1507

AI-Powered DevOps & Site Reliability Engineering

Intelligent Automation, Observability & Reliability Operations

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

Programme Objectives

  • Develop advanced capability to apply AI across DevOps, CI/CD, infrastructure automation, observability, incident response, reliability engineering, and operational improvement.
  • Use AI to analyse logs, metrics, deployment records, alerts, infrastructure configurations, incidents, service-level indicators, and operational data.
  • Build repeatable AI-assisted workflows for deployment analysis, troubleshooting, incident management, infrastructure documentation, automation, and reliability reporting.
  • Apply AI to identify recurring failures, deployment risks, configuration inconsistencies, reliability bottlenecks, abnormal system behaviour, and automation opportunities.
  • Design responsible AI-enabled DevOps and SRE workflows with appropriate controls for security, production access, change management, code validation, auditability, and human oversight.

Tools covered

Generative AI AssistantsAI Search & ResearchCoding AssistantsCI/CD Automation ToolsInfrastructure-as-Code ToolsObservability & Monitoring PlatformsLog & Incident AnalyticsCloud Operations ToolsReliability AnalyticsReporting & Dashboard ToolsWorkflow Automation

Who should attend

  • DevOps Engineers
  • Site Reliability Engineers
  • Senior DevOps Engineers
  • Senior Site Reliability Engineers
  • Cloud Engineers
  • Platform Engineers
  • Infrastructure Engineers
  • DevOps Managers
  • SRE Managers
  • Build & Release Engineers
  • Cloud Operations Engineers
  • Production Support Engineers
  • Automation Engineers
  • Platform Operations Professionals
  • DevOps & SRE Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of DevOps, cloud operations, infrastructure, deployment, production support, or Site Reliability Engineering
  • Familiarity with CI/CD, monitoring, logs, infrastructure configurations, incidents, 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 operational automation
  • Mapping AI opportunities across CI/CD, infrastructure, observability, incident response, and reliability
  • Understanding AI assistance versus authorised production and engineering decisions
  • Recognising hallucination, insecure code, privileged-access, and production-change risks
Practical activities
  • Mapping an existing DevOps / SRE lifecycle
  • Comparing manual and AI-assisted operational activities
  • Creating a DevOps & SRE AI opportunity matrix

Scenarios

Deployment Failure to Reliable Production Recovery

Deployment → Failure Alert → Log & Metric Analysis → Impact Assessment → Incident Response → Root Cause Analysis → Fix / Rollback → Validation → Post-Incident Review

Participants use AI-assisted techniques to analyse a simulated production deployment failure, reconstruct the incident, identify likely causes, coordinate controlled recovery, and document preventive actions.

Reliability Data to Intelligent SRE Operations

Metrics + Logs + Traces + Deployments + Incidents + SLO Data → AI Analysis → Reliability Risks → Priority Actions → Automated Alerts & Routing → SRE Dashboard → Engineering Review

Participants design an AI-enabled DevOps & SRE workflow that consolidates operational signals, identifies recurring reliability issues, automates safe monitoring and follow-up, and improves service resilience while retaining production decisions with authorised engineering professionals.

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