PROGRAMME / 04
Bachelor · Cloud Computing and AI Engineering
Connect cloud architecture, distributed computing and deployment to build AI platforms, manage compute resources and deliver reliable services.
Progress from foundations to professional practice. Planned workload: 180 credits.
YEAR 01Systems and Programming Foundations
- Common Foundation Modules
- Linux and Computer Systems
- Databases and SQL
- Python Programming
- Introduction to Network Applications
- Service Deployment Project
YEAR 02Cloud Platforms and Computing Services
- Computer Networks
- Virtualisation
- Container Technologies
- Cloud Architecture
- Security Foundations
- Introduction to Machine Learning
YEAR 03AI Platforms and Engineering Delivery
- Distributed Systems
- Automated Deployment and Monitoring
- MLOps
- Model Serving
- GPU Computing Foundations
- AI Platform Capstone
SHARED LEARNING
Shared foundations. Specialist depth.
Selected networking, cloud, security, deployment and monitoring modules may be shared with related PTI programmes.
Specialist focus: AI training, inference services and compute platform engineering.
Assessment and outcomes
Assess reproducible deployment, correctness, performance, security, recovery and costs. Submit architecture documentation, deployment files, test records and resource cost analysis.
Project deliverables
Assessment and graduation requirements ↗Deploy a cloud AI application with model serving, version control, monitoring, scaling and recovery mechanisms.